Machine learning algorithms for disease prediction This model has been Mar 13, 2024 · 3. Sep 29, 2020 · Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. In Proceedings of the 2020 International Conference on Electrical and Electronics Engineering (ICE3), Gorakhpur, India, 14–15 February 2020; pp. This study aimed to investigate the application of machine learning techniques for disease prediction. 97% accuracy, machine learning algorithms could identify with 91. Cardiovascular disease and its complications, including dementia, can be averted with early detection. As compared to other models, CNN and SVM have the highest accuracy (99. More than eighty percent of the total population suffers from one or more dental diseases, of which periodontitis, gingivitis, and carcinoma are among them. Results showed that the best performing model was based on Random Forest algorithm with the average accuracy of 87%. Machine learning approaches adapt a set of sophisticated statistical and computational algorithms (e. The key points are: 1. Feb 28, 2024 · The most common cause of death in the world is heart disease. These studies establish the relevance and effectiveness of machine learning algorithms in disease prediction. Here, we present an ensemble machine-learning Another study conducted by Nassif in 2018, the researchers tested Support Vector Machine, Naive Bayes and K-Nearest Neighbour Algorithms using 10-fold cross-validation on the Cleveland Heart Disease data set to compare the performance of three Machine Learning Algorithms for the prediction of Coronary Artery Disease. Identification of Commonly used Algorithm (s)\ Model (s) to Predict IHD. Many researchers have worked on different machine learning algorithms for disease diagnosis. Int. Six algorithms (random forest, K-nearest neighbor, logistic regression, Naïve Bayes, gradient boosting, and AdaBoost classifier) are utilized, with datasets from the Cleveland and IEEE Dataport. Objectives The objectives of this study were to systematically summarize the Dec 1, 2021 · Diabetes is a disease that has no permanent cure; hence early detection is required. The Random Forest classifier achieves F1-Score, AUC, and accuracy values of 84. • Heart Disease Prediction: Several studies have explored the use of machine Jun 1, 2024 · Advanced machine learning algorithms could analyze trends in infection and mortality rates, providing accurate predictions about the spread and severity of the pandemic. , 2019). In this study, data from the 2020 survey presented by the Centers for Disease Control and Prevention (CDC) on the Behavioral Risk Factor Sep 17, 2023 · In order to optimize early prediction and intervention for CVDs, this study proposes the development of novel, robust, effective, and efficient machine learning algorithms, specifically designed for the automatic selection of key features and the detection of early-stage heart disease. [Google Scholar] Deep Learning Applications in Disease Prediction Previous works of disease prediction in genomic data Analysis using non-deep learning approach. Apr 1, 2019 · Using machine learning algorithms can lead to rapid disease prediction with high accuracy. Jan 12, 2024 · Cardiovascular (CVD) and respiratory diseases (RD) have been in the active domain for machine learning (ML) researchers as these diseases significantly contribute to mortality in humans. 2. This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction. The healthcare professional needs useful tools to diagnose medical illnesses. In this work, four supervised machine Feb 25, 2024 · Nichenametla R, Maneesha T, Hafeez S, Krishna H (2018) Prediction of heart disease using machine learning algorithms. This “ability” of AI has attracted wider adoption and application of AI technologies across multiple disciplines, organisations and industries including the health care industry. The early detection and risk assessment of this disease is crucial to the prevention and treatment of the disease. See full list on bmcmedinformdecismak. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. This study enhances heart disease prediction accuracy using machine learning techniques. One dataset was UCI Kaggle Cleveland and the other was Jan 1, 2023 · [5] A. Among the proceedings was a study by Zhang et al. We aim to assess and summarize the overall predictive ability of ML algorithms in Mar 18, 2024 · The most precise version from each disease’s training set of various machine learning algorithms is selected and fed to the given data to trace the best model for multiple disease prediction. Machine learning algorithms are one of the most advanced failed in computer Parkinson Disease Prediction Shubham Bind1, Arvind Kumar Tiwari2, supervised learning, and a family of machine learning algorithms which convert weak learners to strong ones. Reena and Rajkumar gave an explanation of using machine learning algorithms like KNN, naive Bayes, and decision tree for the prediction of heart disease. Shah et al. 7% and 99. A comprehensive search strategy was designed and executed within the MEDLINE, Emb … Dec 15, 2022 · Machine learning algorithms can learn from historical EHR data to identify patterns and make predictions about patient health outcomes. Jan 1, 2021 · This paper studies some of the most widely used machine learning algorithms for heart disease prediction by using the medical data and historical information. The goal or objective of this research is completely related to the prediction of heart disease via a machine learning technique and analysis of them. This study seeks to give a review of Jan 1, 2021 · "Prediction of Cardiovascular Disease using Machine Learning Algorithms" (9) Applied Support Vector Machine , Logistic Regression, Random Forest and Naïve Bayes. Logistic regression is used to carry out computation for prediction. Feb 29, 2024 · The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. Cleveland processed dataset contains 303 instances and 13 predictor variables. Dec 21, 2019 · Disease prediction using health data has recently shown a potential application area for these methods. Disease prediction required a Cardiovascular disease is most significant of the leading causes of mortality in the modern society. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of Aug 29, 2023 · Grampurohit S, Sagarnal C (2020) Disease prediction using machine learning algorithms. The advantage of the proposed system is the use of both structured and unstructured data from real life for data set preparation, which lacks in many of the existing approaches. As a result, disease prediction at an earlier stage becomes a critical task. Keywords- Machine Learning, Heart Mar 27, 2024 · Purpose Machine learning models are used to develop and improve various disease prediction systems. Jan 18, 2024 · 4. & Bahaj, M. As a result, early disease prediction becomes critical. Therefore, this paper proposes a new heart disease classification model based on the support vector machine (SVM) algorithm for improved heart disease detection. Results: A number of risk scores such as the WHO, and PARS models were utilized as the baseline for comparison due to their similar chart-based models. Mar 19, 2021 · The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic Oct 7, 2023 · The use of hybrid machine learning models in the prediction of heart disease offers several distinct advantages over traditional methods or the use of a single machine learning algorithm . Kumar, "Heart Disease Prediction Using Machine Learning Algorithms," 2020 International Conference on Electrical and Electronics Engineering (ICE3), 2020, pp. used predictive data mining techniques for the prediction of cardiovascular disease by evaluating the highest accuracy in the DT among a class of predictive machine learning models such as K-nearest neighbour algorithms, neural network classification, and Bayesian classification algorithms [15, 16]. The analysis of the approaches used and their results is presented in section four. To determine the most accurate model for the prediction of cardiovascular disorders like IHD and locate new risk variables that could cause IHD, many machine-learning algorithms were used. However, there is a rising interest in unsupervised techniques, especially in situations where data labels might be missing — as seen with undiagnosed or rare Sep 29, 2020 · Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. Data mining, machine learning (ML) algorithms, and Neural Network (NN) methods are used in diabetes prediction in our research. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. Machine learning is increasingly being used in medical diagnosis. Effective cardiac treatment requires an accurate heart disease prognosis. Oct 7, 2024 · We used the Synthetic Minority Oversampling Technique (SMOTE) to eliminate inconsistent data and discover the machine learning algorithm that achieves the most accurate heart disease predictions. However, there is no ML method that works universally across a range of diseases, and it remains unclear which ML method suits PD risk prediction best. Further research in this area is Oct 11, 2023 · The table analyzes the accuracy of the various disease prediction models based on machine learning algorithms. More recently, foreseeing heart disease is the stimulating responsibility in the health arena. 1 Introduction Cardiovascular Disease (CVD) is one of the leading positi ons holding diseases 3. In this study, six machine learning (ML) algorithms, viz. On the other hand, making a diagnosis solely on symptoms is too difficult for a doctor. There has been a great deal of research into how ML can improve both the timing and accuracy of diagnosis Jul 31, 2021 · Machine learning in the medical area has become a very important requirement. Google Scholar Balakrishnan M, Selvaraj N, Nagarajan N, Muralidharan B, Mahesh P (2021) Prediction of cardiovascular disease using machine learning. Jan 1, 2023 · For lung disease prediction, many deep learning technologies, including the CNN and the capsule network, are used. conducted on the on machine learning based heart disease prediction. Section three provides information about the widely used machine learning algorithms used for prediction especially for heart attacks and other heart diseases. This abundance of data offers unprecedented opportunities for predictive modeling in precision Dec 31, 2021 · Naïve Bayes classifier is used in the prediction of the disease which is a supervised machine learning algorithm. g. We aimed to build a new optimized ensemble model Jun 20, 2023 · Machine learning models are used to create and enhance various disease prediction frameworks. Jun 17, 2022 · Risk assessment models for T2DM were developed using six machine learning algorithms, including logistic regression (LR), classification and regression tree (CART), artificial neural networks (ANN Jun 17, 2022 · This paper presents six supervised machine learning algorithms such as k-Nearest Neighborhood, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine with radial basis function . They are Logistic Regression, Decision Tree, Random Forest, KNN, SVM, Naive Bayes, and Adaboost. We used the Pima Indian Diabetes (PID) dataset for our research, collected from the UCI Machine Learning Repository. Disease prediction using health data has recently shown a potential application area for these methods. Introduction. J Phys: Conf Ser 1839:012042. Feb 26, 2023 · Currently, machine learning algorithms (MLAs), as a specific artificial intelligence modality, are playing an important role in the field of disease prediction including cardiology mortality With the recent technological advances in microelectronics, wireless communication, machine learning (ML), and decision-making process, Wireless Body Area Network (WBAN) has become the most promising technology. Fourteen attributes and 3000 instances were used in the data set. This research paper analyzes how machine learning techniques are used to predict different diseases and Mar 17, 2022 · Oral diseases are increasing at the same rate as infectious diseases and non-communicable diseases all over the world. Parkinson's disease (PD) is a progressive central nervous system disorder characterized by tremors, vocal cord disorders, bradykinesia, and slurred, slow speech. For classification, preprocessing of data is most important which helps to increase prediction accuracy [17, 18]. 6 million people will die from heart disease in coming 2030. 8741465 The experimental results reveal Random Forest (RF) as the most reliable predictor of heart disease with an accuracy of 83. The most difficult challenge is predicting disease accurately. The enormous amount of information collected by the healthcare field has been established as helpful for using machine learning (ML) to help with judgement and prediction. This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. Dec 31, 2023 · Prior to the implementation of any machine learning algorithms, it is imperative to identify categorical variables. Feb 6, 2023 · They use several machine learning algorithms and methodologies for predicting heart disease. Feb 8, 2023 · Machine-learning algorithms can contribute to the control of infectious diseases by. The datasets have been fixed by imputing the missing values using the MICE imputation method and handling the imbalance properties using the borderline SVMSMOTE method to improve the performance of classifiers. The Machine Learning techniques (26, 27) were applied to Alzheimer's disease datasets to bring a new dimension to predict Disease at an early stage. The predictive power of machine learning is investigated by developing models for heart disease using machine learning. 2020. This paper summarizes the state-of-the-art in machine learning for disease progression modeling and challenges related to this context. Ensemble learning is a machine learning technique that combines many classifiers to increase performance by making more accurate predictions than a single classifier. [ 94 ], where the authors stated that the self-attention methodology applied to various models increased the accuracy of their predictions. , [42–47]). Since early diagnosis of heart disease plays an important role in the survival of individuals, the classification method, which is one of the machine learning techniques, is used in the diagnosis of heart disease and successful results are obtained. Furthermore, AI4S could offer invaluable insights into effective containment strategies by modeling various intervention scenarios, such as social distancing and quarantine measures. Machine learning applications in the medical niche have increased as they can recognize patterns from data. This study aims to identify a tree-based machine learning method with the best performance criteria Sep 27, 2022 · For heart disease prediction, this study implements five machine learning algorithms including Support Vector Machine, Logistic Regression, K-nearest Neighbor, Naive Bayes, and Ensemble Voting Apr 1, 2022 · Despite its recent growth in health care, some studies have used machine learning algorithms to establish the diagnosis and treatment of diseases, managing to assess with reasonable precision the incidence of some diseases, such as diabetes, 9, 10 hypertension, 11 and chronic kidney disease. Deployed prototype source code: Jun 1, 2024 · This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. Heart disease is the deadliest condition in the developed world. 1. Apr 4, 2023 · People nowadays suffer from a variety of diseases because of environmental factors and their lifestyle choices. The raw Alzheimer's disease datasets are inconsistent and redundant, which affects the accuracy of algorithms (28, 29). 1 Data Preprocessing. However, questions arise: which classifier to use? What metrics are appropriate to measure the performance of the classifier? How to Disease Prediction Models hold paramount importance in healthcare for enabling early intervention and improving patient outcomes. 452–457. We begin by introducing The worldwide study on causes of death due to heart disease/syndrome has been observed that it is the major cause of death. It has been shown that predictions and judgments may be made from the massive amounts of data generated by the healthcare industry by using machine learning (ML). An important concern in clinical data processing is the diagnosis of heart disease. Heart disease prediction using machine learning algorithms. [ 12 ] investigated the effectiveness of various supervised learning algorithms, including Random Forest, KNN, Decision Trees, and Naive Bayes, using the Cleveland database from the UCI Machine Learning Repository and selected a subset of 14 attributes. Due to the large number of predictors (i. 9 million lives die because of CVDs every year, which means it is responsible for 31% of all deaths globally. Algorithms based on machine learning have demonstrated remarkable efficacy in Jan 1, 2024 · This paper has focused on eight ensemble learning methods for diagnosing CKD on the UCI machine learning datasets. 1. 9122958. Approximately 6,3 million people worldwide are affected by this disease. Clinical data analysis has significant challenges in predicting heart disease. machine learning for disease prediction is the availability of high-quality, comprehensive. Mar 18, 2024 · In this model, we investigate the application of machine learning techniques for anticipating cardiac disease. Using machine learning in disease prediction would make it easier for doctors to treat patients. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for … Jun 30, 2024 · Recently, machine learning algorithms have shown to be effective tools in the fight against illness prediction due to their superior ability to sort through large datasets in search of complex Jul 1, 2022 · The remainder of the article organize as follows: Section 2 discusses the use of machine learning methods for ailment prediction, section 3 illustrates the work of different researchers for evaluation of the methods used in ML-based CVD prediction, section 4 highlight the gaps of ML model discussed in the literature whether a model is a traditional or hybrid model has some drawback, section 5 Machine learning algorithms are capable to manage huge number of data, to combine data from dissimilar resources, and to integrate the background information in the study [3] . This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies. The probability of the disease is calculated by the Naïve Bayes algorithm. Medically, heart disease is known for being difficult to forecast, detect, and diagnose. 21 %, 88. To treat heart diseases, hospitals and other clinics are giving costly therapies and Nov 7, 2024 · This research focuses on addressing the challenges surrounding interpretability of machine learning techniques in the field of prediction of disease progression. Feb 25, 2022 · The proposed system offers a broad disease prognosis based on patient’s symptoms by using the machine learning algorithms such as convolutional neural network (CNN) for automatic feature Mar 1, 2023 · One of the most difficult challenges in medicine is predicting heart disease at an early stage. J. Three popular machine learning algorithms, Random Forest, Support Vector Machines and Naive Bayes, were employed and their performance was evaluated. Moturi S, Srikanth Vemuru DS (2020) Classification model for prediction of heart disease using correlation coefficient technique. 469%, respectively), making them the best approach. Jan 12, 2023 · We are capable of constructing a collection of prediction models with the help of machine learning and predictive analytics, thanks to the better measures of attributes that can be introduced Jan 8, 2024 · Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. This study ai7ms to identify the key trends among Apr 7, 2021 · The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. Methods: In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. In: 2020 international conference for emerging technology (INCET) (pp 1–7). These techniques were used in many hospitals to improve the early detection of diseases via the use of medical databases for patients. Machine learning algorithms approaches for disease prediction. biomedcentral. Data Keywords: heart disease prediction, machine learning, neural networks, MLP, PSO. The healthcare industry collects large amounts of heart disease data which unfortunately are not “mined” to discover hidden information for effective decision Nov 7, 2024 · This research focuses on addressing the challenges surrounding interpretability of machine learning techniques in the field of prediction of disease progression. Heart failure is a not unusual occasion due to Aug 20, 2024 · Supervised machine learning algorithms have been widely employed for heart disease prediction, demonstrating promising results. In this research, a prediction model utilizing machine learning (ML) was created and verified to evaluate the likelihood of coronary heart disease in individuals affected by periodontitis. 2019. If recent trends are allowed to continue, 23. Let us look into how we can approach this machine-learning problem: The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. It is caused by the death of dopamine-producing neurons and primarily affects Sep 1, 2021 · The study shows a review of recent research on liver disease detection and prediction using various data mining and machine learning techniques and algorithms. Jul 28, 2022 · To develop a risk score, the problem of predicting CVD was solved using a well-designed GA, and finally, the results were compared with classic machine learning (ML) and statistical methods. Classifiers are important to provide tools that can be useful to the health professional for this purpose. The complexity of the human body and the interconnectedness of various health Machine learning techniques, including deep learning models, have advanced rapidly over the past decades, and they have greatly facilitated disease risk prediction and decision making in medicine . IEEE. Moreover, prediction Jan 26, 2024 · Therefore, the goal of this work is to evaluate the performance of different Machine Learning algorithms in order to reduce the high cost of chronic liver disease diagnosis by prediction. May 18, 2022 · In this paper, we streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities. Four out of five CVD deaths are because of heart assaults and strokes, and one-third of these deaths arise upfront in humans below 70 years of age. Data analysis and pattern recognition are 2 further capabilities of machine learning. The research shows that different experimental tools such as Weka, MATLAB, Scikit-Learn, etc. Predicting Heart Disease Using Machine Learning Algorithms. The next part briefly describes these suggested ML formulae for illness diagnosis. Specifically, in the content of this research work, various ML models and Ensemble methods were evaluated and compared in terms of Accuracy, Precision, Recall, F-measure and area under the curve (AUC) in order to predict liver disease occurrence. Mar 26, 2019 · Machine Learning Disease Prediction Models. Sep 3, 2024 · This article aims to implement a robust machine-learning model that can efficiently predict the disease of a human, based on the symptoms that he/she possesses. Naive Bayes and improved K-me ans algorithms are Sep 27, 2022 · In this work, the input is gathered from Cleveland heart disease dataset from UCI machine learning repository to evaluate modified machine learning algorithm. Three popular machine learning algorithms, Random Forest, Support Vector Machines and Naive Dec 29, 2023 · Purpose Disease risk prediction poses a significant and growing challenge in the medical field. While researchers have increasingly utilised machine learning (ML) algorithms to tackle this issue, supervised ML methods remain dominant. Int J Eng Technol 7:363–366. Jan 1, 2022 · Clinical prediction are made based on mortality prediction, length of stay prediction, and ICD-9 code group prediction [20] yielding promising results. However, the current state of real-world research lacks a comprehensive systematic analysis of the predictive performance of machine learning (ML) models for DKD. Plenty of methods have been proposed in disease prediction using genomic data (e. 24%, and 88. for disease prediction using clinical data, emphasizing the importance of feature selection and model optimization techniques. ; Kumar, R. AI systems use various algorithms, especially machine learning (ML), to learn from data and/or experience, make predictions and improve their prediction performance. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. Hybrid models typically combine two or more algorithms to capitalize on the strengths of each, thereby overcoming the limitations that single models may present [ 8 ]. Mehedi Hassan a 1 , Sadika Zaman b 1 , Md. 452-457, doi: 10. Google Scholar Ali F et al (2020) A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion 23:208–222. Diagnosis of Diseases by Using Different Machine Learning Algorithms. Whatever the case, primary treatment usually includes surgery with the aim of completely removing the major tumor mass [ 21 ]; there has been little evidence linking an increased risk for tumors links with mobile phone use [ 22 ]. - sajidifti/Heart_Disease_Detection_ML People suffer from a wide variety of diseases due to their environment and lifestyle factors. As we all know that we are in global pandemic due to Covid-19 situation now, hence, there is a demand occurring in health care services and continuous monitoring. , are used to predict diseases. Additionally, our work adds to the current literature by providing a comprehensive survey of various machine learning algorithms on disease prediction tasks. In this work, eight approved machine-learning techniques were used. The fundamental algorithm is initially practiced in supervised ML techniques on the labeled training dataset. Feb 6, 2023 · The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. In this work, we used a machine learning approach for dental disease prediction in the context of the daily behavior of Heart disease has been a major public health concern in recent years, excessive alcohol consumption, cigarette, and a sedentary lifestyle are the primary factors, and it is the leading cause of mortality among patients. Early prediction of the disease using machine learning (ML) techniques will be the point of interest in this study. Machine Learning, or ML, is becoming increasingly popular in the medical field, particularly in diagnostics and treatment management [1,2]. Sep 11, 2024 · The emergence of biobank-level datasets offers new opportunities to discover novel biomarkers and develop predictive algorithms for human disease. Oct 17, 2024 · To review the literature on heart disease risk prediction, databases such as Springer, ScienceDirect, IEEEXplore, Web of Science, PubMed, MDPI, Hindawi were searched using keywords like heart disease risk, heart disease prediction, data mining, data preprocessing, machine learning algorithms, ensemble classifiers, deep learning algorithms, feature selection, hyperparameter optimization techniques. Therefore, this study investigates the different machine learning algorithms and compares the results using different performance metrics i. Some studies suggest that CVD problems such as cerebrovascular problems, dysrhythmia, inflammatory heart disease, ischemic heart disease (IHD), and RD related problems remain high even after COVID-19 infection Dec 27, 2022 · Cardiovascular disease commonly referred as heart disease, encompasses diverse conditions that the heart undergoes which in turn leads to sudden death or prolonged sickness worldwide over the past decades. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce Nov 19, 2020 · Using that machine learning techniques, we have predicted heart disease and provided a comparative analysis of the algorithms for machine learning used for the experiment of the prediction. , gene transcripts), the main approach in disease detection This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction. 12 Several metrics can be used to evaluate the performance of a predictive algorithm, with the area May 13, 2023 · To apply supervised machine learning algorithms; compare these algorithms and select the best algorithm based on their performance for classification and prediction of type-2 diabetic disease Apr 6, 2019 · mining and machine learning techniques used in heart disease prediction and compare them to find the best method for prediction. Machine Learning Algorithms, with their predictive capabilities, offer promising avenues for enhancing disease prediction accuracy. Heart disease prediction using machine learning and data mining: A review Heart disease prediction is one of the fields where machine learning can be implemented. Feb 18, 2023 · This study aimed to investigate the application of machine learning techniques for disease prediction. Jan 1, 2021 · International Journal of Scientific and Research Publications, Volume 11, Issue 1, January 2021 ISSN 2250-3153 339 Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction Isreal Ufumaka* * Computer Science, University of Benin. , accuracy, precision, recall, f1-score etc. Disease prediction using health data has recently shown a potential application area for Sep 30, 2022 · Few research papers are studied, and therefore, the following inferences are made in accordance with the heart disease prediction. For instance, some of the previous studies Jan 1, 2023 · It was discovered that, when compared to the most experienced physician, who can diagnose with 79. We aim to assess and summarize the overall predictive ability of ML algorithms in cardiovascular diseases. Feb 20, 2023 · Soni et al. 89%, respectively. After Alzheimer's disease, it is the second most prevalent neurological disorder. data. If the data contain outliers, irrelevant features and missing values, then pre diction algorithms cannot measure the correct accuracy. However, supervised machine learning (ML Jan 1, 2021 · Prediction of heart disease using machine learning algorithms 2019 1st international conference on innovations in information and communication technology (ICIICT) ( 2019 ) , 10. Optimizing One of the most common causes for death in modern society is heart disease. A weighted K-nearest neighbor model using feature scores is proposed in this study to increase May 6, 2021 · In the proposed model, the data are entered into an android app, the analysis is then performed in a real-time database using a pretrained machine learning model which was trained on the same dataset and deployed in firebase, and finally, the disease detection result is shown in the android app. 1109/ICE348803. , 2016; Vinayakumar et al. We investigate a large dataset made up of patient details, such as demographics Machine learning prediction in cardiovascular diseases: primary outcome was a composite of the predictive ability of ML algorithms of coronary artery disease, heart failure, stroke, and May 16, 2024 · In this study, the researcher employs machine learning such as SVM (support vector machine), random forest, logistic regression, Naïve Bayes, and deep learning algorithms' such as LSTM (long Jul 10, 2021 · The document summarizes a disease prediction system for rural health services presented by two students. Although several researchers have employed ensemble techniques for disease prediction, a comprehensive comparative study of these An empirical study on machine learning algorithms for heart disease prediction (Tsehay Admassu Assegie) 1067 (SVM), logistic regression (LR), decision tree (DT), and random forest (RF). In recent eras, every minute approximately one person expires due to heart ailment. To increase prediction accuracy, the χ2 statistical optimum feature selection technique Apr 1, 2024 · Efficient prediction of coronary artery disease using machine learning algorithms with feature selection techniques ☆ Author links open overlay panel Md. Section five People nowadays suffer from a variety of diseases because of environmental factors and their lifestyle choices. Google Scholar They proposed two machine-learning algorithms, random forest and eXtreme gradient boosting (XGBoost), for the analysis and prediction of hand, foot, and mouth disease . Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. 1% correctness [10]. Sep 29, 2022 · Automatic heart disease prediction is a major global health concern. Supervised machine learning algorithms have been a dominant method in the data mining field. 17, 19 The prevalence of machine learning algorithms in the healthcare industry is growing as a direct result of the rapid growth of Feb 25, 2022 · This paper proposed a method of identification and prediction of the presence of chronic disease in an individual using the machine learning algorithms such as CNN and KNN. Feb 13, 2024 · Singh, A. The healthcare sector is poised to benefit significantly from harnessing massive data and the insights we can derive from it, underscoring the importance of integrating machine learning (ML) to improve CVD prevention Dec 29, 2023 · Purpose Disease risk prediction poses a significant and growing challenge in the medical field. This paper evaluates six machine learning algorithms to see how well they predict heart disease However, we also demonstrate that popular deep learning models for disease prediction are not meaningfully better than simpler, more interpretable classifiers such as XGBoost. Dec 16, 2020 · For disease prediction, we use K-Nearest Neighbor (KNN) and Convolutional neural network (CNN) machine learning algorithms for the accurate prediction of disease. Mushfiqur Rahman c , Anupam Kumar Bairagi a , Walid El-Shafai d e , Rajkumar Singh Rathore f , Deepak Gupta g Mar 21, 2024 · Despite medical advancements in recent years, cardiovascular diseases (CVDs) remain a major factor in rising mortality rates, challenging predictions despite extensive expertise. This notebook uses 7 ML algorithms. No data preprocessing carried out Jun 1, 2021 · Machine learning algorithms are adopted to create a prediction model since ML methods allow computers to learn and gain intelligence from previous experience or a pre-defined dataset (Gulshan et al. 5. , Support vector machine (SVM) or Random forest) to make predictions by mathematically mapping the complex associations between a set of risk SNPs to complex disease phenotypes (Quinlan, 1990; Wei et Apr 14, 2023 · In the medical domain, early identification of cardiovascular issues poses a significant challenge. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms. , logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used to analyze two heart disease datasets. However, supervised machine learning (ML Oct 12, 2022 · Ali MdM et al (2021) Heart disease prediction using supervised machine learning algorithms: performance analysis and comparison, vol 136. e. While individual disease prediction models have made notable strides, a crucial gap persists in the realm of multi-disease prediction. 1109/iciict1. Singh and R. Jan 1, 2021 · By this, machine learning algorithms (logistic linear regression, decision tree classifier, Gaussian Naïve Bayes models) will be developed to predict the presence of heart diseases in patients Liver Disease Prediction Using Machine Learning Algorithms Abstract: In Human beings, Liver is the most primary part of the body that performs many functions including the production of Bile, excretion of bile and bilirubin, metabolism of proteins and carbohydrates, activation of Enzymes, Storing glycogen, vitamins, and minerals, plasma proteins synthesis and clotting factors. An approximate of 17. machine learning algorithms for disease prediction. Sep 27, 2024 · Cardiovascular disease (CVD) can often lead to serious consequences such as death or disability. In order to solve this problem, data mining is considered as an important and effective method and heuristic approaches, pale in comparison to the potential that machine learning algorithms bring to the table. In disease prediction tasks, ensemble learning, which Dec 18, 2023 · Khourdifi, Y. 1, 16 Because there is such a wide diversity of health datasets, machine learning algorithms are the most appropriate method for enhancing the accuracy of diagnosis prediction. Feb 4, 2022 · The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. com The proposed system offers a broad disease prognosis based on patient's symptoms by using the machine learning algorithms such as convolutional neural network (CNN) for automatic feature extraction and disease prediction and K-nearest neighbor (KNN) for distance calculation to find the exact match in the data set and the final disease Sep 13, 2022 · This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the Jul 16, 2024 · Patients require a disease prediction model with the help of various supervised ML algorithms such as RF, DT, KNN, ANN, NN, SVM, NLP, and many more, allowing health officials and doctors to take preventative measures that can reliably, accurately, and efficiently predict diseases [20, 21]. With the technological advancements occurring in the world, particularly in the medical field, it was important to utilize machine learning algorithms due to their significance in health reality and illness prediction. The predictive model can identify and understand the incoming data, allowing it to make more precise decisions. Int J 9(2). Keywords: Machine learning, Supervised machine learning algorithm, Medical data, Disease prediction Background A machine learning algorithm for predicting heart disease. The number one reason of deaths worldwide are cardiovascular diseases. We experiment the modified prediction models over real Nov 29, 2023 · Background: The association between periodontitis and cardiovascular disease is increasingly recognized. Dec 23, 2023 · Background Machine learning is increasingly recognized as a viable approach for identifying risk factors associated with diabetic kidney disease (DKD). While researchers have increasingly utilised machine learning (ML) algorithms to tackle this issue Oct 23, 2022 · Background: Supervised machine learning algorithms have been a dominant method in the data mining field. 52% compared to other supervised machine learning algorithms. Machine learning techniques are explicitly used to illness datasets to extract features for optimal illness diagnosis, prediction, prevention, and therapy. In this research, we explore the application of various machine learning techniques for disease prediction. Table 2 Summary of efficiency of various algorithms to predict the diseases Jun 1, 2022 · Keywords heart disease prediction; machine learning al gorit hms; web application; health param eters.
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