Abstract

Heart-related conditions remain the foremost global cause of mortality. In 2000, heart disease claimed around 14 million lives worldwide, a number that surged to approximately 620 million by 2023. The aging and expanding population significantly contribute to this rising mortality trend. However, this also underscores the potential for significant impact through early intervention, crucial for reducing fatalities from heart failure, where prevention plays a pivotal role. The aim of the present research is to develop a prospective ML framework that can detect important features and predict cardiac conditions as an early stage using a variety of choice of features strategies. The Features subsets that were chosen were designated as FST1, FST2, and FST3, respectively. Three distinct methods, including correlation-based feature selection, chi-square and mutual information, were used for picking features. Next, the most confident theory & the most appropriate feature selection were identified using six alternative machine learning models: Logistical Regression (LR) (AL1), the support vector  Machine (SVM ) (AL2), K-nearest neighbor (K-NN)  (AL3), Random forest (RF) model (AL4), Naive Bayes (NB) model (AL5), and Decision Tree (DT) (AL6). Ultimately, we discovered that, with 95.25% accuracy, 95.11% sensitivity, 95.23% specificity, 96.96 area below receiver operating characteristic and 0.27 log loss, the random forest model offered the most excellent results for F3 feature sets. No one has investigated coronary artery disease forecasting in depth; however, our study evaluates multiple statistics (specificity, sensitivity, accuracy, AUROC, and log loss) and uses multiple attribute choices to improve algorithms success for important features. The suggested model has considerable promise for medical use to speculate CVD find in Precursor at a minimal cost and in a shorter amount of time as well as will assist limited experience physician to take right decision based on the results of the used model combined with specific criteria.

Keywords

ML Technique, Feature Selection Method, Categorization and Modelling, Data Interpretation, Random Forest,

Downloads

Download data is not yet available.

References

  1. A.S. Deepika, N. Jaisankar, Detecting and Classifying Myocardial Infarction in Echocardiogram Frames with an Enhanced CNN Algorithm and ECV-3D Network. IEEE Access, 12, (2024) 51690-51703. https://doi.org/10.1109/ACCESS.2024.3385787
  2. M.S. Amin, Y.K. Chiam, K.D. Varathan, Identification of significant feature and data mining techniques in predicting heart disease. Telematics and Informatics, 36, (2019) 82-93. https://doi.org/10.1016/j.tele.2018.11.007
  3. T. Ullah, S.I. Ullah, K. Ullah, M. Ishaq, A. Khan, Y.Y. Ghadi, A. Algarni, Machine Learning-Based Cardiovascular Disease Detection Using Optimal Feature Selection. IEEE Access, 12, (2024) 16431-16446. https://doi.org/10.1109/ACCESS.2024.3359910
  4. P.A. Heidenreich, J.G. Trogdon, O.A. Khavjou, J. Butler, K. Dracup, M.D. Ezekowitz, E.A. Finkelstein, Y. Hong, S.C. Johnston, A. Khera, D.M. Lloyd-Jones, S.A. Nelson, G. Nichol, D. Orenstein, P.W.F. Wilson, Y.J. Woo, Forecasting the future of cardiovascular disease in the United States A Policy Statement From the American Heart Association. Circulation, 123(8), (2011) 933–944. https://doi.org/10.1161/CIR.0b013e31820a55f5
  5. G. Savarese, L.H. Lund, Global public health burden of heart failure. Cardiac failure review, 3(1), (2017) 7–11. https://doi.org/10.15420/cfr.2016:25:2
  6. C. Beyene, P. Kamat, Survey on prediction and analysis the occurrence of heart disease using data mining techniques. International Journal of Pure and Applied Mathematics,118(8), (2018) 165-174,
  7. V.V. Ramalingam, A. Dandapath, M.K. Raja, Heart dis ease prediction using machine learning techniques: a survey. International Journal of Engineering & Technology, 7(2.8), (2018) 684–687. https://doi.org/10.14419/ijet.v7i2.8.10557
  8. S. Pouriyeh, S. Vahid, G. Sannino, G. De Pietro, H. Arabnia, J. Gutierrez, (2017) A comprehensive investigation and comparison of machine learning techniques in the domain of heart disease. IEEE Symposium on Computers and Communications, IEEE, Greece. https://doi.org/10.1109/ISCC.2017.8024530
  9. E. Fix, J.L. Hodges, Discriminatory analysis. Nonpara metric discrimination: consistency properties. International Statistical Review / Revue Internationale de Statistique, 57(3), (1989) 238–247. https://doi.org/10.2307/1403797
  10. S. Palaniappan, R. Awang, (2008) Intelligent heart disease pre diction system using data mining techniques. IEEE/ACS International Conference on Computer Systems and Applications, IEEE, Qatar. https://doi.org/10.1109/AICCSA.2008.4493524
  11. U. Haq, J.P. Li, M.H. Memon, S. Nazir, R. Sun, A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mobile Information Systems, 2018, (2018) 1-22. https://doi.org/10.1155/2018/3860146
  12. J. Cheng, G. Li, X. Chen, Research on travel time prediction model of freeway based on gradient boosting decision tree, IEEE Access, 7, (2019) 7466-7480. https://doi.org/10.1109/ACCESS.2018.2886549
  13. D. Stojanov, E. Lazarova, E. Veljkova, P. Rubartelli, M. Giacomini, Predicting the outcome of heart failure against chronic-ischemic heart disease in elderly population–Machine learning approach based on logistic regression, case to Villa Scassi hospital Genoa, Italy. Journal of King Saud University-Science, 35(3), (2023) 102573. https://doi.org/10.1016/j.jksus.2023.102573
  14. R. Yilmaz F.H. Yagin, C. Colak, K. Toprak, N. Abdel Samee, N.F. Mahmoud, A.A. Alshahrani, Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study. Frontiers in Medicine, 11, (2024) 1285067. https://doi.org/10.3389/fmed.2024.1285067
  15. P. Lakshmi Prabha, A.K. Jayanthy, C. Prem Kumar, B. Ramraj, Prediction of cardiovascular risk by measuring carotid intima media thickness from an ultrasound image for type II diabetic mellitus subjects using machine learning and transfer learning techniques. The Journal of Supercomputing, 77, (2021) 10289-10306. https://doi.org/10.1007/s11227-021-03676-w
  16. P. Ghosh, S. Azam, M. Jonkman, A. Karim, F.J.M. Shamrat, E. Ignatious, S. Shultana, A.R. Beeravolu De F. Boer, Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques. IEEE Access, 9, (2021) 19304-19326. https://doi.org/10.1109/ACCESS.2021.3053759
  17. S.S. Yadav, S.M. Jadhav, Detection of common risk factors for diagnosis of cardiac arrhythmia using machine learning algorithm. Expert systems with applications, 163, (2021) 113807. https://doi.org/10.1016/j.eswa.2020.113807
  18. S. Razia, J.C. Babu, K.H. Baradwaj, K.S.S.R. Abhinay, M. Anusha, Heart disease prediction using machine learning techniques. International Journal of Recent Technology and Engineering, 8(4), (2019) 10316–10320. http://www.doi.org/10.35940/ijrte.D4537.118419
  19. A. Degerli, M. Zabihi, S. Kiranyaz, T. Hamid, R. Mazhar, R. Hamila, M. Gabbouj, Early Detection of Myocardial Infarction in Low-Quality Echocardiography. IEEE Access, 9, (2021) 34442–34453. https://doi.org/10.1109/ACCESS.2021.3059595
  20. D. Mienye, Y. Sun, Effective feature selection for improved prediction of heart disease. In Pan-African Artificial Intelligence and Smart Systems Conference, 405, (2022) 94–107. https://doi.org/10.1007/978-3-030-93314-2_6
  21. V. Vakharia, V.K. Gupta, P.K. Kankar, A comparison of feature ranking techniques for fault diagnosis of ball bearing, Soft Computing, 20(4), (2015) 1601–1619. https://doi.org/10.1007/s00500-015-1608-6
  22. N. Carrara, J. Ernst, On the estimation of mutual information. Proceedings, 33(1), (2020) 31. https://doi.org/10.3390/proceedings2019033031
  23. T. Akter, M.S. Satu, M.I. Khan, M.H. Ali, S. Uddin, P. Lio, J.M.W. Quinn, M.A. Moni, Machine learning-based models for early stage detection of autism spectrum disorders. IEEE Access, 7, (2019) 166509-166527. https://doi.org/10.1109/ACCESS.2019.2952609
  24. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, É. Duchesnay, (2011). Scikit-learn: Machine learning in Python. Journal of machine Learning research, 12, 2825-2830.
  25. S. Mohan, C. Thirumalai, G. Srivastava, Effective heart disease prediction using hybrid machine learning techniques, IEEE Access, 7, (2019) 81542–81554. https://doi.org/10.1109/ACCESS.2019.2923707
  26. M.S. Amin, Y.K. Chiam, K.D. Varathan, Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics, 36, (2019) 82–93. https://doi.org/10.1016/j.tele.2018.11.007
  27. C.B.C. Latha, S. C. Jeeva, Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Informatics in Medicine Unlocked, 16, (2019) 1-9, https://doi.org/10.1016/j.imu.2019.100203
  28. J. Patel, D. Tejal Upadhyay S. Patel, Heart disease prediction using machine learning and data mining technique. Heart Disease, 7(1), (2015) 129-137.