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

Year : 2024 | Volume :13 | Issue : 01 | Page : 29-43
By

Amit Arya

Vineet Richhariya

Sadhna K. Mishra

  1. Research Scholar Lakshmi Narain College of Technology M. P India
  2. Professor Lakshmi Narain College of Technology M. P India
  3. HOD Lakshmi Narain College of Technology M. P India

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: Heart failure, Cardiovascular, Risk factor, Machine learning, Cardiovascular Disease.

[This article belongs to Research & Reviews : Journal of Medical Science and Technology(rrjomst)]

How to cite this article: Amit Arya, Vineet Richhariya, Sadhna K. Mishra. Review on Machine Learning Techniques for Heart Failure Analysis in Health Industries. Research & Reviews : Journal of Medical Science and Technology. 2024; 13(01):29-43.
How to cite this URL: Amit Arya, Vineet Richhariya, Sadhna K. Mishra. Review on Machine Learning Techniques for Heart Failure Analysis in Health Industries. Research & Reviews : Journal of Medical Science and Technology. 2024; 13(01):29-43. Available from: https://journals.stmjournals.com/rrjomst/article=2024/view=143486




References

  1. Bora, S. Gutta, and A. Hadaegh, “Using machine learning to Predict Heart Disease,” WSEAS Trans. Biol. Biomed., vol. 19, pp. 1–9, 2022, doi: 10.37394/23208.2022.19.1.
  2. Chicco and G. Jurman, “Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone,” BMC Med. Inform. Decis. Mak., 2020, doi: 10.1186/s12911-020-1023-5.
  3. Agrawal, J. Chandiwala, S. Agrawal, and Y. Goyal, “Heart Failure Prediction using Machine Learning with Exploratory Data Analysis,” in 2021 International Conference on Intelligent Technologies, CONIT 2021, 2021. doi: 10.1109/CONIT51480.2021.9498561.
  4. Awan SE, Sohel F, Sanfilippo FM, Bennamoun M, Dwivedi G. Machine learning in heart failure: ready for prime time. Curr Opin Cardiol. 2018 Mar;33(2):190–195. doi: 10.1097/HCO.0000000000000491.
  5. Probst, M. N. Wright, and A. L. Boulesteix, “Hyperparameters and tuning strategies for random forest,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2019. doi: 10.1002/widm.1301.
  6. Grgić, D. Mušić, and E. Babović, “Model for predicting heart failure using Random Forest and Logistic Regression algorithms,” IOP Conf. Ser. Mater. Sci. Eng., 2021, doi: 10.1088/1757-899x/1208/1/012039.
  7. Taylan O, Alkabaa AS, Alqabbaa HS, Pamukçu E, Leiva V. Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods. Biology (Basel). 2023 Jan 11;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 Mar 24;141(12):1034-1036. doi: 10.1161/CIRCULATIONAHA.119.043392.
  9. McCartan, R. Mason, S. R. Jayasinghe, and L. R. Griffiths, “Cardiomyopathy classification: Ongoing debate in the genomics era,” Biochemistry Research International. 2012. doi: 10.1155/2012/796926.
  10. L. McClelland et al., “10-Year Coronary Heart Disease Risk Prediction Using Coronary Artery Calcium and Traditional Risk Factors Derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) with Validation in the HNR (Heinz Nixdorf Recall) Study and the DHS (Dallas Heart Study),” J. Am. Coll. Cardiol., 2015 Oct 13;66(15):1643–53, 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. Eur Heart J Qual Care Clin Outcomes. 2021 Jul 21;7(4):330–339. doi: 10.1093/ehjqcco/qcab029.
  13. Bays HE, Kulkarni A, German C, Satish P, Iluyomade A, Dudum R, Thakkar A, Rifai MA, Mehta A, Thobani A, Al-Saiegh Y, Nelson AJ, Sheth S, Toth PP. Ten things to know about ten cardiovascular disease risk factors-2022. Am J Prev Cardiol. 2022 Apr 6;10:100342. doi: 10.1016/j.ajpc.2022.100342.
  14. D. Flora and M. K. Nayak, “A Brief Review of Cardiovascular Diseases, Associated Risk Factors and Current Treatment Regimes,” Curr. Pharm. Des., 2019;25(38):4063–4084doi: 10.2174/1381612825666190925163827.
  15. A. Roth et al., “Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study,” Journal of the American College of Cardiology. 2020. doi: 10.1016/j.jacc.2020.11.010.
  16. Grana, N. Benowitz, and S. A. Glantz, “E-cigarettes: A scientific review,” Circulation. 2014. 13;129(19):1972–86.doi: 10.1161/CIRCULATIONAHA.114.007667.
  17. Bösner et al., “Accuracy of symptoms and signs for coronary heart disease assessed in primary care,” Br. J. Gen. Pract., 2010, Jun;60(575):e246–57. 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, “A few useful things to know about machine learning,” Communications of the ACM. 2012 vol 55Issue 10 PP 79-87. doi: 10.1145/2347736.2347755.
  20. Aggarwal and Anmol, “Health Insurance Amount Prediction Using Supervised Learning,” in Proceedings of International Conference on Technological Advancements in Computational Sciences, ICTACS 2022, 2022. doi: 10.1109/ICTACS56270.2022.9988256 https://ieeexplore.ieee.org/document/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, X. Wu, L. Duan and S. Gao, “Domain Adversarial Reinforcement Learning for Partial Domain Adaptation,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, pp. 539–553, doi: 10.1109/TNNLS.2020.3028078.
  23. Nashif, M. R. Raihan, M. R. Islam, and M. H. Imam, “Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System,” World J. Eng. Technol., Vol.6 Issue 4.Pg 854–873,2018, 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. A. Almazroi, E. A. Aldhahri, S. Bashir, and S. Ashfaq, “A Clinical Decision Support System for Heart Disease Prediction Using Deep Learning,” IEEE Access, 2023, VOLUME 11, 2023: 61646–61659 doi: 10.1109/ACCESS.2023.3285247.
  26. S. E. Reddy, S. R. Sripathi, D. Akula, S. Palaniswamy and S. R, “Cardiovascular Disease Prediction using Machine Learning and Deep Learning,” 2022 6th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, India, 2022, pp. 1-5. doi: 10.1109/CSITSS57437.2022.10026391.
  27. Mamun, M. M. Uddin, V. Kumar Tiwari, A. M. Islam and A. U. Ferdous, “MLHeartDis:Can Machine Learning Techniques Enable to Predict Heart Diseases?,” 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, NY, USA, 2022, pp. 0561–0565, doi: 10.1109/UEMCON54665.2022.9965714.
  28. Saravanan and K. Swaminathan, “Hybrid K-Means and Support Vector Machine to Predict Heart Failure,” 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2021, pp. 1678–1683, doi: 10.1109/ICOSEC51865.2021.9591738.. doi:
  29. L. Kumar and B. E. Reddy, “Heart Disease Detection System Using Gradient Boosting Technique,” 2021 International Conference on Computing Sciences (ICCS), Phagwara, India, 2021, pp. 228–233, doi: 10.1109/ICCS54944.2021.00052.
  30. Julian, R. Deepika, B. Geetha, and V. J. Sweety, “Heart disease prediction using machine learning,” Artif. Intell. Blockchain, Comput. Secur. Vol. 2, vol. 2, no. 04, pp. 248–253, 2023, doi: 10.1201/9781032684994-38.
  31. M. Qadri, A. Raza, K. Munir and M. S. Almutairi, “Effective Feature Engineering Technique for Heart Disease Prediction With Machine Learning,” in IEEE Access, vol. 11, pp. 56214-56224, 2023, doi: 10.1109/ACCESS.2023.3281484 doi: 10.1109/ACCESS.2023.3281484.
  32. Saboor, M. Usman, S. Ali, A. Samad, M. F. Abrar, and N. Ullah, “A Method for Improving Prediction of Human Heart Disease Using Machine Learning Algorithms,” Mob. Inf. Syst., 2022, 22, Article ID 1410169, 9 pages doi: 10.1155/2022/1410169.
  33. Kavitha, G. Gnaneswar, R. Dinesh, Y. R. Sai, and R. S. Suraj, “Heart Disease Prediction using Hybrid machine Learning Model,” in Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021, 2021., 2021, pp. 1329-1333 doi: 10.1109/ICICT50816.2021.9358597.
  34. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, and P. Singh, “Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning,” Comput. Intell. Neurosci., 2021, Volume 2021 | Article ID 8387680doi: 10.1155/2021/8387680.
  35. Atallah and A. Al-Mousa, “Heart Disease Detection Using Machine Learning Majority Voting Ensemble Method,” 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS), Amman, Jordan, 2019, pp. 1-6, doi: 10.1109/ICTCS.2019.8923053.

Regular Issue Subscription Review Article
Volume 13
Issue 01
Received February 23, 2024
Accepted February 29, 2024
Published April 16, 2024