STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews : A Journal of Medical Science and Technology Review Article

Review on Machine Learning Techniques for Heart Failure Analysis in Health Industries

  1. Amit Arya Lakshmi Narain College of Technology
  2. Vineet Richhariya Lakshmi Narain College of Technology
  3. Sadhna K. Mishra Lakshmi Narain College of Technology

Abstract

There are few bodily components as crucial as the heart. It aids in the filtration and distribution of blood to every area of a body. The world's biggest cause of death is heart disease. It has been reported that symptoms include breathing difficulties, fast heartbeat, and chest discomfort. They analyze this data on a regular basis. This review begins with a brief introduction of cardiac disease and the present methods used to treat it. It also provides a concise overview of the most important ML methods currently published for the forecasting of CVD. Data analytics is helpful for making predictions with more data, and it aids the medical Center in forecasting a variety of ailments. The monthly data retention rate is quite high. The collected information may serve as a foundation for disease outbreak prediction. Predictions and judgements have become feasible because to the massive amounts of data produced by the healthcare business. Cardiovascular disease prediction and prevention is the greatest data analytic problem. The abundance of data generated by healthcare facilities has prompted the development of machine learning algorithms that can make accurate forecasts and sound decisions. The area of Machine Learning (ML) within AI focuses on teaching computers new skills and tasks with little to no human oversight. Finding patterns and making predictions are the goals of data analysis and statistical approaches. This research compared many Machine Learning models to find the most effective one for making more accurate predictions of cardiovascular disease (CVD). Finally, the survey delves into several research gaps and difficulties, providing researchers with valuable information to inspire better future work on HD prediction using ML models.

Keywords

References (35)

  1. Bora N, Gutta S, Hadaegh A. Using Machine Learning to Predict Heart Disease. WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE. 2022;19:1-9. doi:10.37394/23208.2022.19.1
  2. Chicco D, Jurman G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making. 2020;20(1). doi:10.1186/s12911-020-1023-5
  3. Agrawal H, Chandiwala J, Agrawal S, Goyal Y. Heart Failure Prediction using Machine Learning with Exploratory Data Analysis. 2021 International Conference on Intelligent Technologies (CONIT). 2021:1-6. doi:10.1109/conit51480.2021.9498561
  4. Awan SE, Sohel F, Sanfilippo FM, Bennamoun M, Dwivedi G. Machine learning in heart failure. Current Opinion in Cardiology. 2018;33(2):190-195. doi:10.1097/hco.0000000000000491
  5. Probst P, Wright MN, Boulesteix AL. Hyperparameters and tuning strategies for random forest. WIREs Data Mining and Knowledge Discovery. 2019;9(3). doi:10.1002/widm.1301
  6. Grgić V, Mušić D, Babović E. Model for predicting heart failure using Random Forest and Logistic Regression algorithms. IOP Conference Series: Materials Science and Engineering. 2021;1208(1):012039. doi:10.1088/1757-899x/1208/1/012039
  7. Taylan O, Alkabaa A, Alqabbaa H, Pamukçu E, Leiva V. Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods. Biology. 2023;12(1):117. doi:10.3390/biology12010117
  8. Kaltman JR, Burns KM, Pearson GD, Goff DC, Evans F. Disparities in Congenital Heart Disease Mortality Based on Proximity to a Specialized Pediatric Cardiac Center. Circulation. 2020;141(12):1034-1036. doi:10.1161/circulationaha.119.043392
  9. McCartan C, Mason R, Jayasinghe SR, Griffiths LR. Cardiomyopathy Classification: Ongoing Debate in the Genomics Era. Biochemistry Research International. 2012;2012:1-10. doi:10.1155/2012/796926
  10. McClelland RL, Jorgensen NW, Budoff M, Blaha MJ, Post WS, Kronmal RA, et al. 10-Year Coronary Heart Disease Risk Prediction Using Coronary Artery Calcium and Traditional Risk Factors. Journal of the American College of Cardiology. 2015;66(15):1643-1653. doi:10.1016/j.jacc.2015.08.035
  11. Ahmad, G. N., Fatima, H., Ullah, S., & Saidi, A. S. (2022). Efficient medical diagnosis of human heart diseases using machine learning techniques with and without GridSearchCV. IEEE Access, 10, 80151–80173.
  12. Harrison SL, Buckley BJR, Rivera-Caravaca JM, Zhang J, Lip GYH. Cardiovascular risk factors, cardiovascular disease, and COVID-19: an umbrella review of systematic reviews. European Heart Journal - Quality of Care and Clinical Outcomes. 2021. doi:10.1093/ehjqcco/qcab029
  13. Bays HE, Agarwala A, German C, Satish P, Iluyomade A, Dudum R, et al. Ten things to know about ten cardiovascular disease risk factors – 2022. American Journal of Preventive Cardiology. 2022;10:100342. doi:10.1016/j.ajpc.2022.100342
  14. Flora GD, Nayak MK. A Brief Review of Cardiovascular Diseases, Associated Risk Factors and Current Treatment Regimes. Current Pharmaceutical Design. 2019;25(38):4063-4084. doi:10.2174/1381612825666190925163827
  15. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019. Journal of the American College of Cardiology. 2020;76(25):2982-3021. doi:10.1016/j.jacc.2020.11.010
  16. Grana R, Benowitz N, Glantz SA. E-Cigarettes. Circulation. 2014;129(19):1972-1986. doi:10.1161/circulationaha.114.007667
  17. Bösner S, Becker A, Hani MA, Keller H, Sönnichsen AC, Haasenritter J, et al. Accuracy of symptoms and signs for coronary heart disease assessed in primary care. British Journal of General Practice. 2010;60(575):e246-e257. doi:10.3399/bjgp10x502137
  18. Deepika, “A novel heart disease prediction system using machine learning algorithms,” 2022. https://shodhganga.inflibnet.ac.in/handle/10603/466923
  19. Domingos P. A few useful things to know about machine learning. Communications of the ACM. 2012;55(10):78-87. doi:10.1145/2347736.2347755
  20. Aggarwal S, Anmol. Health Insurance Amount Prediction Using Supervised Learning. 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS). 2022:578-581. doi:10.1109/ictacs56270.2022.9988256
  21. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer..
  22. Chen J, Wu X, Duan L, Gao S. Domain Adversarial Reinforcement Learning for Partial Domain Adaptation. IEEE Transactions on Neural Networks and Learning Systems. 2022;33(2):539-553. doi:10.1109/tnnls.2020.3028078
  23. Nashif S, Raihan MR, Islam MR, Imam MH. Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System. World Journal of Engineering and Technology. 2018;06(04):854-873. doi:10.4236/wjet.2018.64057
  24. Xiaoyu Sun, Yuzhe Yin, Qiwei Yang and Tianqi Huo,Artifcial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives, European Journal of Medical Research (2023) 28:242.
  25. Almazroi AA, Aldhahri EA, Bashir S, Ashfaq S. A Clinical Decision Support System for Heart Disease Prediction Using Deep Learning. IEEE Access. 2023;11:61646-61659. doi:10.1109/access.2023.3285247
  26. Reddy TSE, Sripathi SR, Akula D, Palaniswamy S, R S. Cardiovascular Disease Prediction using Machine Learning and Deep Learning. 2022 6th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). 2022:1-5. doi:10.1109/csitss57437.2022.10026391
  27. Mamun M, Uddin MM, Kumar Tiwari V, Islam AM, Ferdous AU. MLHeartDis:Can Machine Learning Techniques Enable to Predict Heart Diseases? 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). 2022:0561-0565. doi:10.1109/uemcon54665.2022.9965714
  28. Saravanan S, Swaminathan K. Hybrid K-Means and Support Vector Machine to Predict Heart Failure. 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC). 2021:1678-1683. doi:10.1109/icosec51865.2021.9591738
  29. Kumar KL, Reddy BE. Heart Disease Detection System Using Gradient Boosting Technique. 2021 International Conference on Computing Sciences (ICCS). 2021:228-233. doi:10.1109/iccs54944.2021.00052
  30. Julian A, Deepika R, Geetha B, Jeslin Sweety V. Heart disease prediction using machine learning. Artificial Intelligence, Blockchain, Computing and Security Volume 2. 2023:248-253. doi:10.1201/9781032684994-38
  31. Qadri AM, Raza A, Munir K, Almutairi MS. Effective Feature Engineering Technique for Heart Disease Prediction With Machine Learning. IEEE Access. 2023;11:56214-56224. doi:10.1109/access.2023.3281484
  32. Saboor A, Usman M, Ali S, Samad A, Abrar MF, Ullah N. A Method for Improving Prediction of Human Heart Disease Using Machine Learning Algorithms. Mobile Information Systems. 2022;2022:1-9. doi:10.1155/2022/1410169
  33. Kavitha M, Gnaneswar G, Dinesh R, Sai YR, Suraj RS. Heart Disease Prediction using Hybrid machine Learning Model. 2021 6th International Conference on Inventive Computation Technologies (ICICT). 2021:1329-1333. doi:10.1109/icict50816.2021.9358597
  34. Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P. Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning. Computational Intelligence and Neuroscience. 2021;2021(1). doi:10.1155/2021/8387680
  35. Atallah R, Al-Mousa A. Heart Disease Detection Using Machine Learning Majority Voting Ensemble Method. 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS). 2019:1-6. doi:10.1109/ictcs.2019.8923053
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