Heart Disease Prediction; a Machine Learning Approach
Keywords:
Heart Disease, Machine Learning, Artificial intelligence, Machine learning Predictive analyticsAbstract
Machine learning has been one of the most widely used tools in medical science. It has shown promising application in disease detection as well as in disease prediction. In this paper we focus on the artificial intelligence-based heart disease prediction system using machine learning algorithms. We discuss various algorithms that have been employed by researchers in healthcare sector and chalk out the comparative analysis among these various algorithms. Traditional clinical diagnosis methods have their own limitations, with the help of our study we would be focusing on accuracy and ability to detect the heart disease at early stage. We have discussed some most recent works in heart disease prediction and done a comparative analysis on the accuracy and fusibility of these methods.
References
Mehra R 2007 Global public health problem of sudden cardiac death Journal of electrocardiology 40 S118-S22
Okrainec K, Banerjee D K and Eisenberg M J 2004 Coronary artery disease in the developing world American heart journal 148 7-15
Rayner M, Allender S, Scarborough P and Group B H F H P R 2009 Cardiovascular disease in Europe European Journal of Cardiovascular Prevention & Rehabilitation 16 S43-S7
Hollan I, Dessein P, Ronda N, Wasko M, Svenungsson E, Agewall S, Cohen-Tervaert J, Maki-Petaja K, Grundtvig M and Karpouzas G 2015 Prevention of cardiovascular disease in rheumatoid arthritis Autoimmunity reviews 14 952-69
Huang S, Li J, Shearer G C, Lichtenstein A H, Zheng X, Wu Y, Jin C, Wu S and Gao X 2017 Longitudinal study of alcohol consumption and HDL concentrations: a community-based study The American journal of clinical nutrition 105 905-12
Vasheghani-Farahani A, Nouri N, Seifirad S, Sheikh Fathollahi M, Hakki E, Alidoosti M, Davoodi G, Masoudkabir F and Poorhosseini H 2013 Comparison of cardiovascular risk factors and biochemical profile in patients with cardiac syndrome X and obstructive coronary artery disease: A propensity score-matched study ARYA atherosclerosis 9 269-73
Sergi G, Veronese N, Fontana L, De Rui M, Bolzetta F, Zambon S, Corti M-C, Baggio G, Toffanello E D and Crepaldi G 2015 Pre-frailty and risk of cardiovascular disease in elderly men and women: the Pro. VA study Journal of The American college of cardiology 65 976-83
Ahmed S, Shaikh S, Ikram F, Fayaz M, Alwageed H S, Khan F and Jaskani F H 2022 Prediction of Cardiovascular Disease on Self-Augmented Datasets of Heart Patients Using Multiple Machine Learning Models Journal of Sensors 2022
Ma J-L, Cui Y-L and Dong M-C 2013 An effective low-complexity multi-vital-signs compression technique for embedded-link e-home healthcare. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE) pp 1177-81
Zeinalnezhad M, Chofreh A G, Goni F A, Klemeš J J and Sari E 2020 Simulation and improvement of patients’ workflow in heart clinics during COVID-19 pandemic using timed coloured petri nets International Journal of Environmental Research and Public Health 17 8577
Chang V, Bhavani V R, Xu A Q and Hossain M 2022 An artificial intelligence model for heart disease detection using machine learning algorithms Healthcare Analytics 2 100016
Learning M 2017 Heart disease diagnosis and prediction using machine learning and data mining techniques: a review Advances in Computational Sciences and Technology 10 2137-59
Priya A, Garg S and Tigga N P 2020 Predicting anxiety, depression and stress in modern life using machine learning algorithms Procedia Computer Science 167 1258-67
Shailaja K, Seetharamulu B and Jabbar M 2018 Machine learning in healthcare: A review. In: 2018 Second international conference on electronics, communication and aerospace technology (ICECA): IEEE) pp 910-4
Khan F A, Zeb K, Al-Rakhami M, Derhab A and Bukhari S A C 2021 Detection and prediction of diabetes using data mining: a comprehensive review IEEE Access 9 43711-35
Li M and Zhou Z-H 2007 Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 37 1088-98
Barrett M, Boyne J, Brandts J, Rocca B-L, De Maesschalck L, De Wit K, Dixon L, Eurlings C, Fitzsimons D and Golubnitschaja O 2019 Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care Epma Journal 10 445-64
Paradis V, Cossette S, Frasure-Smith N, Heppell S and Guertin M-C 2010 The efficacy of a motivational nursing intervention based on the stages of change on self-care in heart failure patients Journal of Cardiovascular Nursing 25 130-41
Olsen C R, Mentz R J, Anstrom K J, Page D and Patel P A 2020 Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure American Heart Journal 229 1-17
Shouman M, Turner T and Stocker R 2011 Using decision tree for diagnosing heart disease patients. In: Proceedings of the Ninth Australasian Data Mining Conference-Volume 121, pp 23-30
Deekshatulu B and Chandra P 2013 Classification of heart disease using k-nearest neighbor and genetic algorithm Procedia technology 10 85-94
Gupta A, Kumar L, Jain R and Nagrath P 2020 Heart disease prediction using classification (naive bayes). In: Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019): Springer) pp 561-73
Pal M and Parija S 2021 Prediction of heart diseases using random forest. In: Journal of Physics: Conference Series: IOP Publishing) p 012009
Dhanka S and Maini S 2021 Random Forest for Heart Disease Detection: A Classification Approach. In: 2021 IEEE 2nd International Conference On Electrical Power and Energy Systems (ICEPES): IEEE) pp 1-3
Shah S M S, Shah F A, Hussain S A and Batool S 2020 Support vector machines-based heart disease diagnosis using feature subset, wrapping selection and extraction methods Computers & Electrical Engineering 84 106628
Son Y-J, Kim H-G, Kim E-H, Choi S and Lee S-K 2010 Application of support vector machine for prediction of medication adherence in heart failure patients Healthcare informatics research 16 253-9
Ambrish G, Ganesh B, Ganesh A, Srinivas C and Mensinkal K 2022 Logistic Regression Technique for Prediction of Cardiovascular Disease Global Transitions Proceedings
Zhang Y, Diao L and Ma L 2021 Logistic Regression Models in Predicting Heart Disease. In: Journal of Physics: Conference Series: IOP Publishing) p 012024
Sireesha V, Hegde N P, Nanditha P and Naresh B R 2022 Smart Intelligent Computing and Applications, Volume 2: Springer) pp 181-90
Patel J, TejalUpadhyay D and Patel S 2015 Heart disease prediction using machine learning and data mining technique Heart Disease 7 129-37
Lichman M 2013 UCI machine learning repository. Irvine, CA, USA)
Bhalerao S and Gunjal B 2013 Hybridization of improved k-means and artificial neural network for heart disease prediction Int. J. Comput. Sci. Trends Technol 4 5461
Bertsimas D, Mingardi L and Stellato B 2021 Machine learning for real-time heart disease prediction IEEE Journal of Biomedical and Health Informatics 25 3627-37
Sahoo S, Kanungo B, Behera S and Sabut S 2017 Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities Measurement 108 55-66
Haq A U, Li J P, Memon M H, Nazir S and Sun R 2018 A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms Mobile Information Systems 2018
Nashif S, Raihan M R, Islam M R and Imam M H 2018 Heart disease detection by using machine learning algorithms and a real-time cardiovascular health monitoring system World Journal of Engineering and Technology 6 854-73
Seh A H and Chaurasia P K 2019 A review on heart disease prediction using machine learning techniques Journal Homepage: http://www. ijmra. us 9
Ramalingam V, Dandapath A and Raja M K 2018 Heart disease prediction using machine learning techniques: a survey International Journal of Engineering & Technology 7 684-7
Sujatha P and Mahalakshmi K 2020 Performance evaluation of supervised machine learning algorithms in prediction of heart disease. In: 2020 IEEE International Conference for Innovation in Technology (INOCON): IEEE) pp 1-7
Al’Aref S J, Anchouche K, Singh G, Slomka P J, Kolli K K, Kumar A, Pandey M, Maliakal G, Van Rosendael A R and Beecy A N 2019 Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging European heart journal 40 1975-86
Ali M M, Paul B K, Ahmed K, Bui F M, Quinn J M and Moni M A 2021 Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison Computers in Biology and Medicine 136 104672
Rani P, Kumar R, Ahmed N M and Jain A 2021 A decision support system for heart disease prediction based upon machine learning Journal of Reliable Intelligent Environments 7 263-75
Amen K, Zohdy M and Mahmoud M 2020 Machine learning for multiple stage heart disease prediction. In: Proceedings of the 7th International Conference on Computer Science, Engineering and Information Technology, pp 205-23
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