Decision Support System for Reliable Prediction of Heart Disease using Machine Learning Techniques: An Exhaustive Survey and Future Directions

Decision Support System for Reliable Prediction of Heart Disease using Machine Learning Techniques: An Exhaustive Survey and Future Directions
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Deepali Yewale, S. P. Vijayaragavan, Mousami Munot
DOI :  10.14445/22315381/IJETT-V70I4P228

How to Cite?

Deepali Yewale, S. P. Vijayaragavan, Mousami Munot, "Decision Support System for Reliable Prediction of Heart Disease using Machine Learning Techniques: An Exhaustive Survey and Future Directions," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 316-331, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P228

Abstract
The Centers for Disease Control and Prevention statistics say 17.9 million people died from cardiovascular diseases (CVD), representing 32% of global deaths. This will increase and may reach 50% in 2050. CVD continue to be the prominent cause of mortality globally, making early detection of heart disease critical. Previously, knowledge-centred clinical decision support systems were created, which applied medical professionals` expertise and manually transferred data into computer systems. This procedure is time-consuming and is highly reliant on the judgment of a medical professional, which may be subjective. Machine learning (ML) algorithms have been applied to solve this problem by automatically gaining information from raw data. This study aims to thoroughly review the decision support system (DSS) using the ML approach for the CVD prediction for the University of California Irvin (UCI) dataset. Firstly, the exhaustive survey is carried out to understand and study the approaches adopted by different researchers. In the preceding sections, a few important aspects of heart disease study are discussed, including Risk factors of heart disease, Types of heart disease, ML approaches in the design of prediction systems, and optimization techniques for performance improvement. The surveyed papers are evaluated using different performance matrices. After that, I discovered the literature gaps and presented them in the comparative analysis section. This survey will assist investigators who wish to use ML or data mining approach in Heart disease projection.

Keywords
Cardiovascular disease, Heart disease prediction, Machine Learning, Decision support system.

Reference
[1] Ks. Reddy, B. Shah, C. Varghese, and A. Ramadoss, Responding to the Threat of Chronic Diseases in India, the Lancet. 366(9498) (2005) 1744-1749.
[2] S. Mendis, P. Puska, and B. Norrving, Global Atlas on Cardiovascular Disease Prevention and Control, World Health Organization. 15(1) (2011) 1-163.
[3] (2020) The Who Website. [Online]. Available: Https://Www.Who.Int/News-Room/Fact-Sheets/Detail/the-Top-10-Causes-of-Death.
[4] (2017) The Wikipedia Website. [Online]. Available: Https://En.Wikipedia.Org/Wiki/List_of_Causes_of_Death_by_Rate#Cite_Note-3.
[5] (2019) Centers for Disease Control and Prevention. [Online]. Available: Https://Www.Cdc.Gov/Heartdisease/Statistics_Maps.Htm.
[6] P.K. Anooj, Clinical Decision Support System: Risk Level Prediction of Heart Disease Using Weighted Fuzzy Rules, Journal of King Saud University-Computer and Information Sciences.24(1) (2012) 27-40.
[7] M. C. Tu, D. Shin, and D. Shin, A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms, Eighth Ieee International Conference on Dependable, Autonomic and Secure Computing,(2009)183-187.
[8] G. Subbalakshmi, K. Ramesh, and M.C. Rao, Decision Support in Heart Disease Prediction System Using Naive Baye, Indian Journal of Computer Science and Engineering (Ijcse). 2(2) (2011) 170-176.
[9] L. Parthiban, and R. Subramanian, Intelligent Heart Disease Prediction System using Canfis and Genetic Algorithm, International Journal of Biological, Biomedical and Medical Sciences. 3(3) (2008) 157-160.
[10] D. Shanthi, G. Sahoo, and N. Saravanan, Input Feature Selection Using the Hybrid Neuro-Genetic Approach in Diagnosing Stroke Disease, Ijcsns. 8(12) (2008) 99-107.
[11] H. Yan, J. Zheng, Y. Jiang, C. Peng, and Q. Li, Development of A Decision Support System for Heart Disease Diagnosis using Multilayer Perceptron, Proceedings of International Symposium on Circuits and Systems, Iscas`03, 5 (2003) 709-712.
[12] M. Kukar, I. Kononenko, and C. Grošelj, Modern Parameterization and Explanation Techniques in the Diagnostic Decision Support System: A Case Study in Diagnostics of Coronary Artery Disease, Artificial Intelligence in Medicine. 52(2) (2011) 77-90.
[13] (2021) Medical News Today. [Online]. Available: Https://Www.Medicalnewstoday.Com/Articles/323724.
[14] Safdar, S. Zafar, N. Zafar, and N. F. Khan, Machine Learning-Based Decision Support Systems (Dss) for Heart Disease Diagnosis: A Review, Artificial Intelligence Review. 50(4) (2018) 597-623.
[15] (2019) Centers for Disease Control and Prevention. [Online]. Available: Https://Www.Cdc.Gov/Heartdisease/Risk_Factors.Htm.
[16] R. Chitra, and V. Seenivasagam, Heart Disease Prediction System using Supervised Learning Classifier, Bonfring International Journal of Software Engineering and Soft Computing. 3(1) 2013) 1-7.
[17] A. Mathur, and G.P. Moschis, Socialization Influences on Preparation for Later Life, Journal of Marketing Practice: Applied Marketing Science, 5 (6/7/8) (1999) 163 -176.
[18] L. P. Kaelbling, M. L. Littman, and A. W. Moore, Reinforcement Learning: A Survey, Journal of Artificial Intelligence Research. 4 (1996) 237-285.
[19] M.A. Hearst, S.T. Dumais, E. Osuna, J. Platt, and B. Schoellkopf, Support Vector Machines, Ieee Intelligent Systems and their Applications. 13(4), (1998) 18-28.
[20] T. Hastie, R. Tibshirani, and J. Friedman, Additive Models, Trees, and Related Methods, the Elements of Statistical Learning Springer Series in Statistics, Second Edition, New York,(2009) 295-336.
[21] O. Kramer, K-Nearest Neighbors. in: Dimensionality Reduction with Unsupervised Nearest Neighbors. Intelligent Systems Reference Library, Springer, Berlin, 51(1) (2013) 13-23.
[22] S. Chen, G.I. Webb, L. Liu, and X. Ma, A Novel Selective Naïve Bayes Algorithm, Knowledge-Based Systems, 192(105361) (2020) 112.
[23] G. Subbalakshmi, K. Ramesh, and M.C. Rao, Decision Support in Heart Disease Prediction System Using Naive Bayes, Indian Journal of Computer Science and Engineering (Ijcse). 2(2) (2011) 170-176.
[24] Y. Liu, Y. Wang, J. Zing, the New Machine Learning Algorithm: Random Forest, Ser. Lecture Notes in Computer Science. Springer, Berlin. 7473(1) (2012) 246-252.
[25] (2021) Investopedia. [Online]. J. Chen, Available: Https://Www.Investopedia.Com/Terms/N/Neuralnetwork.Asp.
[26] P. Sharma, and R. Bhartiya, Implementation of Decision Tree Algorithm to Analysis the Performance, International Journal of Advanced Research in Computer and Communication Engineering. 1(10) (2012) 861-864.
[27] (1999) Mit-Bih Polysomnographic Database.[Online]. Available: Https://Archive.Physionet.Org/Physiobank/Database/Slpdb/.
[28] (1988) Heart Disease Dataset Uci. [Online]. Available:Https://Archive.Ics.Uci.Edu/Ml/Datasets/Heart+Disease,
[29] P. Rani, R. Kumar, N.S. Ahmed, and A. Jain, A Decision Support System for Heart Disease Prediction Based upon Machine Learning, Journal of Reliable Intelligent Environments. 1(13) (2021) 1-13.
[30] M. Diwakar, A. Tripathi, K. Joshi, M. Memoria, and P Singh, Latest Trends on Heart Disease Prediction using Machine Learning and Image Fusion, Materials Today: Proceedings. 37 (2021) 3213-3218.
[31] R. Katarya, and Sk. Meena, Machine Learning Techniques for Heart Disease Prediction: A Comparative Study and Analysis, Health and Technology. 11(1) (2021) 87-97.
[32] S. Prakash, K. Sangeetha, and N. Ramkumar, An Optimal Criterion Features Selection Method for Prediction and Effective Analysis of Heart Disease, Cluster Computing. 22(5) (2019) 11957-11963.
[33] F. Ali, S. El-Sappagh, S.M. Islam., D. Kwak, A. Ali, M. Imran, and K.S. Kwak, A Smart Healthcare Monitoring System for Heart Disease Prediction Based on Deep Ensemble Learning and Feature Fusion, Information Fusion.63 (2020) 208-222.
[34] Nl Fitriyani, M Syafrudin, G Alfian, J Rhee. Hdpm: An Effective Heart Disease Prediction Model for a Clinical Decision Support System. Ieee Access, 8, (2020) 133034-133050.
[35] A.K. Gárate-Escamila, A.H. El Hassani, and E. Andrès, Classification Models for Heart Disease Prediction Using Feature Selection and Pca, Informatics in Medicine Unlocked 19(100330) (2020) 1-11.
[36] N. Hasan and Y. Bao, Comparing Different Feature Selection Algorithms for Cardiovascular Disease Prediction, Health and Technology Springer Journal. 10(1) (2020) 1-14.
[37] C. Latha and S. Jeeva, Improving the Accuracy of Prediction of Heart Disease Risk Based on Ensemble Classification Techniques, Informatics in Medicines Unlocked. 16(100203) (2019)1-9.
[38] I. D. Mienye, Y. Sun, Z. Wang, An Improved Ensemble Learning Approach for the Prediction of Heart Disease Risk, Informatics in Medicines Unlocked. 20 (100402) (2020) 1-5.
[39] A. Rahim, Y. Rasheed, F. Azam, M. W. Anwar, M. A. Rahim, A. W. Muzaffar, An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases, Ieee Open Access. 9 (2021) 106575-106588.
[40] Li Yang. A.Shami, on Hyper Parameter Optimization of Machine Learning Algorithms: Theory and Practice, Neurocomputing Elsevier Journal. 415 (2020) 295-316.
[41] Ek Hashi, Md Zaman, Developing A Hyperparameter Tuning Based Machine Learning Approach of Heart Disease Prediction, Journal of Applied Science and Process Engineering.7(2) (2020) 631-647.
[42] A. Javeed, S. Zhou, An Intelligent Learning System Based on Random Search Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection, Ieee Access. 7 (2019) 180235-180243.
[43] B. Dun, E. Wang, and S. Majumder, Heart Disease Diagnosis on Medical Data Using Ensemble Learning,Stanford.Edu, 1(1) (2016) 1-5.
[44] Ds.Medhekar, M.P. Bote, and S.D. Deshmukh, Heart Disease Prediction System Using Naive Bayes, Int. J. Enhanced Res. Sci. Technol. Engineering. 2 (3) (2013)1-5.
[45] W. Wiharto, H. Kusnanto, and H. Herianto, Performance Analysis of Multiclass Support Vector Machine Classification for Diagnosis of Coronary Heart Diseases, International Journal on Computational Science & Applications (Ijcsa).5 (5) (2015) 27-37.
[46] N. Khateeb, and M. Usman, Efficient Heart Disease Prediction System Using K-Nearest Neighbour Classification Technique, Proceedings of the International Conference on Big Data and Internet of Thing, (2017) 21-26.
[47] A.H. Chen, S.Y. Huang, P.S. Hong, C.H. Cheng, and E.J. Lin, Hdps: Heart Disease Prediction System, Computing in Cardiology. (2011) 557-560.
[48] Sk. Sen, Predicting and Diagnosing Heart Disease Using Machine Learning Algorithms, International Journal of Engineering and Computer Science. 6(6) (2017) 21623-21631.
[49] S. Pouriyeh, S. Vahid, G. Sannino, G. De Pietro, H. Arabnia, and J. Gutierrez, A Comprehensive Investigation and Comparison of Machine Learning Techniques in Heart Disease, Ieee Symposium on Computers and Communications (Iscc), (2017) 204-207.
[50] Yk. Singh, N. Sinha, and S.K. Singh, Heart Disease Prediction System Using Random Forest, International Conference on Advances in Computing and Data Sciences Springer, (2017) 613-623.
[51] R. Das, I. Turkoglu, and A. Sengur, Effective Diagnosis of Heart Disease Through Neural Networks Ensembles, Expert Systems With Applications. 36(4) (2009) 7675-7680.
[52] Nb. Amma, Cardiovascular Disease Prediction System Using Genetic Algorithm and Neural Network, International Conference on Computing, Communication and Applications, (2012) 1-5.
[53] T. Santhanam, E.P. Ephzibah, Heart Disease Classification Using Pca and Feed-Forward Neural Networks, Mining Intelligence and Knowledge Exploration, Springer, Switzerland, (2013) 90-99.
[54] In Press, K. Budholiya, S. Shrivastava, V. Sharma, An Optimized Xgboost Based Diagnostic System for Effective Prediction of Heart Disease, Journal of King Saud University –Computer and Information Sciences.
[55] S. P. Patro, G.S. Nayak, N. Padhy, Heart Disease Prediction by Using Novel Optimization Algorithm: A Supervised Learning Prospective, Informatics in Medicine Unlocked. 26 (100696) (2021) 1-17.
[56] R. Valarmathi, T. Sheela, Heart Disease Prediction Using Hyperparameter Optimization (Hpo) Tuning, Biomedical Signal Processing and Control, Elsevier. 103033 (2021) 1-10.