Abstract

Among the toughest assignments for medical professionals is discovering heart illness indicators as quickly as attainable. Coronary artery disease is an urgent issue and should be treated promptly. The diagnosis of heart illness is complicated by a number of factors affecting health, including high pressure, situated cholesterol levels, inconsistent heartbeat, and several more. Therefore, AI can be helpful in recognising and dealing with ailments at an early stage. This research suggests an ensemble-based method to estimate an individual's risk of heart disease using Deep Learning (DL) and Machine Learning (ML) models. In order to forecast cardiovascular disease, we use six classification methods. A large collection of cardiovascular disease cases that is made open to the world is used to train models. To identify key characteristics related to cardiac illness, we employ Random Forest (RF). The research's results show that the ML ensemble model obtains the highest accuracy of 92.75% in predicting diseases. When compared to conventional machine learning methods like K-nearest neighbor (KNN), Random Forest (RF), and Multi-Layer Perceptrons (MLP), the suggested methodology's uniqueness is evaluated by showing a 5.52% increase in efficiency.

Keywords

Heart Failure, ML Model, Deep Learning, Ensemble Techniques, Random Forest,

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References

  1. National Institutes of Health (2000) the Practical Guide: Identification, Evaluation and Treatment of Overweight and Obesity in Adults. National Institutes of Health.
  2. D. Deng, Jiao, P., Ye, X., & Xia, L. An Image‐Based Model of the Whole Human Heart with Detailed Anatomical Structure and Fiber Orientation. Computational and Mathematical Methods in Medicine, 2012(1), (2012) 891070. https://doi.org/10.1155/2012/891070
  3. M. Elhneiti, M. Al-Hussami, Predicting risk factors of heart disease among Jordanian patients. Health, 9(02), (2017) 237-251. https://doi.org/10.4236/health.2017.92016
  4. World Health Organization, Cardiovascular diseases World Health Organization. World Health Organization, Switzerland
  5. K.V. Sabarish, T.S. Parvati, An experimental investigation on L9 orthogonal array with various concrete materials. Materials Today: Proceedings, 37(2), (2021) 3045-3050. https://doi.org/10.1016/j.matpr.2020.09.005
  6. C.J. Harrison, C.J. Sidey-Gibbons, Machine learning in medicine: a practical introduction to natural language processing. BMC medical research methodology, 21(1), (2021) 158. https://doi.org/10.1186/s12874-021-01347-1
  7. N. Kumar, Gulshan, S. Kaur, Review paper on machine learning algorithms, International Journal of Scientific Research in Engineering and Management, (2024), 1–5. http://dx.doi.org/10.55041/ijsrem34900
  8. L.E. Sucar, E.F. Morales, J. Hoey, Decision theory models for applications in artificial intelligence: Concepts and solutions. IGI Global, (2012) 1-8. https://doi.org/10.4018/978-1-60960-165-2.ch001
  9. M. Pérez-Ortiz, S. Jiménez-Fernández, P. Gutiérrez, E. Alexandre, C. Hervás-Martínez, S. Salcedo-Sanz, A review of classification problems and algorithms in Renewable Energy applications. Energies, 9(8), (2016) 607. https://doi.org/10.3390/en9080607
  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. R. Kumar, P. Kumar, R. Tripathi, G.P. Gupta, A.K.M.N. Islam, M. Shorfuzzaman Permissioned blockchain and deep learning for secure and efficient data sharing in industrial healthcare systems. IEEE Transactions on Industrial Informatics, 18(11), (2022) 8065–8073. https://doi.org/10.1109/TII.2022.3161631
  12. P.N. Dawadi, D.J. Cook, M. Schmitter-Edgecombe, Automated cognitive health assessment using smart home monitoring of complex tasks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(6), (2013) 1302–1313. https://doi.org/10.1109/TSMC.2013.2252338
  13. P. Rani, R. Kumar, N.M.O.S. Ahmed, A.A. Jain, decision support system for heart disease prediction based upon machine learning. Journal of Reliable Intelligent Environments, 7(3), (2021) 263–275. https://doi.org/10.1007/s40860-021-00133-6
  14. P. Motarwar, A. Duraphe, G. Suganya, M. Premalatha, Cognitive approach for heart disease prediction using machine learning. 2020 International Conference on Emerging Trends in Information Technology and Engineering, IEEE, India. https://doi.org/10.1109/ic-ETITE47903.2020.242
  15. I.M. Nasser, S.S. Abu-Naser, Lung cancer detection using artificial neural network. International Journal of Engineering and Information Systems (IJEAIS), 3(3), (2019) 17-23.
  16. U. Ahmed, S.K. Mukhiya, G. Srivastava, Y. Lamo, J.C.W. Lin, Attention-based deep entropy active learning using lexical algorithm for mental health treatment. Front Psychol, 12, (2021). https://doi.org/10.3389/fpsyg.2021.642347
  17. N.S. El_Jerjawi, S.S. Abu-Naser, Diabetes prediction using artificial neural network. International Journal of Advanced Science and Technology, 121, (2018) 54-64.
  18. M.G. Feshki, O.S. Shijani. (2016) Improving the heart disease diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network. Artificial Intelligence and Robotics (IRANOPEN), IEEE, Iran. https://doi.org/10.1109/RIOS.2016.7529489
  19. M.N.R. Chowdhury, E. Ahmed, M.A.D. Siddik, A.U. Zaman, (2021) Heart disease prognosis using machine learning classification techniques. In: 2021 6th International Conference for Convergence in Technology (I2CT), IEEE, India.
  20. 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
  21. A.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 System, (2018), 1–21. https://doi.org/10.1155/2018/3860146
  22. G. Renugadevi, G.A. Priya, B.D. Sankari, R. Gowthamani, Predicting heart disease using hybrid machine learning model. In Journal of Physics: Conference Series, IOP Publishing, 1916(1), (2021) 012208. https://doi.org/10.1088/1742-6596/1916/1/012208
  23. Oswald, G. Jaya Sathwika, A. Bhattacharya, Prediction of Cardiovascular disease (CVD) using ensemble learning algorithms. CODS-COMAD '22: Proceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD), (2022) 292 – 293. https://doi.org/10.1145/3493700.3493747
  24. 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
  25. I. D. Mienye, Y. Sun, Effective feature selection for improved prediction of heart disease. Pan-African Artificial Intelligence and Smart Systems, 405, (2022) 94–107. https://doi.org/10.1007/978-3-030-93314-2_6
  26. V. Vakharia, V.K. Gupta, P.K. Kankar, A comparison of feature ranking techniques for fault diagnosis of ball bearing, Soft Computing, 20(4), (2016) 1601–1619. https://doi.org/10.1007/s00500-015-1608-6
  27. A. Abbasi, A.R. Javed, C. Chakraborty, J. Nebhen, W. Zehra, Z. Jalil, ElStream, An ensemble learning approach for concept drift detection in dynamic social big data stream learning. IEEE Access, 9, (2021) 66408–66419. https://doi.org/10.1109/ACCESS.2021.3076264
  28. W .Zehra, A.R. Javed, Z. Jalil, H.U. Khan, T.R. Gadekallu, Cross corpus multi-lingual speech emotion recognition using ensemble learning. Complex & Intelligent Systems, 7(4), (2021) 1845-1854. https://doi.org/10.1007/s40747-020-00250-4
  29. X. Dong, Z. Yu, W. Cao, Y. Shi, Q. Ma, A survey on ensemble learning. Frontiers of Computer Science, 14, (2020) 241-258. https://doi.org/10.1007/s11704-019-8208-z
  30. S.B. Imandoust, M. Bolandraftar, Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background. International journal of engineering research and applications, 3(5), (2013) 605-610.
  31. E.K. Sahin, Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Applied Science, 2(7), (2020) 1308. https://doi.org/10.1007/s42452-020-3060-1
  32. V. Sharma, S. Yadav, M. Gupta, (2020) Heart disease prediction using machine learning techniques. International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), IEEE, India. https://doi.org/10.1109/ICACCCN51052.2020.9362842
  33. J.C.T. Arroyo, A.J.P. Delima, An optimized neural network using genetic algorithm for cardiovascular disease prediction. Advances in Information Technology, 13(1), (2022) 95-99.
  34. A. Alfaidi, R. Aljuhani, B. Alshehri, H. Alwadei, S. Sabbeh, Machine learning: assisted cardiovascular diseases diagnosis. International Journal of Advanced Computer Science and Applications, 13(2), (2022) 135-141. https://doi.org/10.14569/IJACSA.2022.0130216