Optimizing Heart Attack Prediction Through OHE2LM: A Hybrid Modelling Strategy
Main Article Content
Abstract
Predicting heart attacks stands as a significant concern contributing to global morbidity. Within clinical data analysis, cardiovascular disease emerges as a pivotal focus for forecasting, wherein Data Science and machine learning (ML) offer invaluable tools. These methodologies aid in predicting heart attacks by considering various risk factors Just like high blood pressure, increased cholesterol levels, irregular pulse rates, and diabetes, this research aims to enhance the accuracy of predicting heart disease through machine learning techniques.This study introduces a MLdriven approach, termed ML-ELM, dedicated to forecasting heart attacks by analysing diverse risk factors. The proposed ML-ELM model is compared with alternative Utilizing machine learning techniques like Support Vector Machines, Logistic Regression, Naïve Bayes, and XGBoost is a key aspect of this exploration into different approaches for predictive modeling., is part of the research strategy. The dataset utilized for heart disease symptoms is sourced from the UCI ML Repository. The outcomes reveal that our proposed ML-ELM model has demonstrated superior predictive performance among the ML techniques tested. ML models show notable efficiency in identifying heart attack symptoms, particularly with boosting algorithms. Accuracy assessments were employed to gauge the predictive ability, Our suggested model demonstrated an outstanding accuracy rate of 96.77%.
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
D. Chicco and G. Jurman, “Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone,” BMC medical informatics and decision making, vol. 20, no. 1, pp. 1–16, 2020.
T. Obasi and M. O. Shafiq, “Towards comparing and using Machine Learning techniques for detecting and predicting Heart Attack and Diseases,” in 2019 IEEE international conference on big data (big data), 2019, pp. 2393–2402.
A. U. Haq, J. P. Li, M. H. Memon, S. Nazir, and R. Sun, “A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms,” Mobile Information Systems, vol. 2018, 2018.
V. V Ramalingam, A. Dandapath, and M. K. Raja, “Heart disease prediction using machine learning techniques: a survey,” International Journal of Engineering & Technology, vol. 7, no. 2.8, pp. 684–687, 2018.
A. M. Alaa, T. Bolton, E. Di Angelantonio, J. H. Rudd, and M. van der Schaar, “Cardiovascular Disease Risk Prediction Using Machine Learning: A Prospective Cohort Study of 423,604 Participants,” Circulation, vol. 138, no. Suppl_1, pp. A15234--A15234, 2018.
M. Chen, Y. Hao, K. Hwang, L. Wang, and L. Wang, “Disease prediction by machine learning over big data from healthcare communities,” Ieee Access, vol. 5, pp. 8869–8879, 2017.
M. Aljanabi, M. H. Qutqut, and M. Hijjawi, “Machine learning classification techniques for heart disease prediction: a review,” International Journal of Engineering & Technology, vol. 7, no. 4, pp. 5373–5379, 2018.
D. Swain, S. K. Pani, and D. Swain, “A metaphoric investigation on prediction of heart disease using machine learning,” in 2018 International conference on advanced computation and telecommunication (ICACAT), 2018, pp. 1–6.
S. F. Weng, J. Reps, J. Kai, J. M. Garibaldi, and N. Qureshi, “Can machine-learning improve cardiovascular risk prediction using routine clinical data?,” PloS one, vol. 12, no. 4, p. e0174944, 2017.
Y. Khan, U. Qamar, N. Yousaf, and A. Khan, “Machine learning techniques for heart disease datasets: a survey,” in Proceedings of the 2019 11th International Conference on Machine Learning and Computing, 2019, pp. 27–35.
J. I. Z. Chen and P. Hengjinda, “Early prediction of coronary artery disease (CAD) by machine learning method-a comparative study,” Journal of Artificial Intelligence, vol. 3, no. 01, pp. 17–33, 2021.
Bhatt CM, Patel P, Ghetia T, Mazzeo PL. Effective Heart Disease Prediction Using Machine Learning Techniques. Algorithms. 2023; 16(2):88. https://doi.org/10.3390/a16020088
Li, F., Wu, P., Ong, H. H., Peterson, J. F., Wei, W. Q., & Zhao, J. (2023). Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction. Journal of Biomedical Informatics, 138, 104294.
Kresoja, K. P., Unterhuber, M., Wachter, R., Thiele, H., & Lurz, P. (2023). A cardiologist’s guide to machine learning in cardiovascular disease prognosis prediction. Basic research in cardiology, 118(1), 10.
Bizimana, P. C., Zhang, Z., Asim, M., El-Latif, A., & Ahmed, A. (2023). An Effective Machine Learning-Based Model for an Early Heart Disease Prediction. BioMed Research International, 2023.
Abdulsalam, G., Meshoul, S., & Shaiba, H. (2023). Explainable Heart Disease Prediction Using Ensemble-Quantum Machine Learning Approach. Intell. Autom. Soft Comput, 36, 761-779.
Dileep, P., Rao, K. N., Bodapati, P., Gokuruboyina, S., Peddi, R., Grover, A., & Sheetal, A. (2023). An automatic heart disease prediction using cluster-based bi-directional LSTM (C-BiLSTM) algorithm. Neural Computing and Applications, 35(10), 7253-7266.
Sk, K. B., Roja, D., Priya, S. S., Dalavi, L., Vellela, S. S., & Reddy, V. (2023, March). Coronary Heart Disease Prediction and Classification using Hybrid Machine Learning Algorithms. In 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA) (pp. 1-7). IEEE.
Nandy, S., Adhikari, M., Balasubramanian, V., Menon, V. G., Li, X., & Zakarya, M. (2023). An intelligent heart disease prediction system based on swarm-artificial neural network. Neural Computing and Applications, 35(20), 14723-14737.
Narayan, Vipul, and A. K. Daniel. "Novel protocol for detection and optimization of overlapping coverage in wireless sensor networks." Int. J. Eng. Adv. Technol 8 (2019).
Narayan, Vipul, A. K. Daniel, and Ashok Kumar Rai. "Energy efficient two tier cluster based protocol for wireless sensor network." 2020 international conference on electrical and electronics engineering (ICE3). IEEE, 2020.
Narayan, Vipul, and A. K. Daniel. "Multi-tier cluster based smart farming using wireless sensor network." 2020 5th international conference on computing, communication and security (ICCCS). IEEE, 2020.
Narayan, Vipul, and A. K. Daniel. "RBCHS: Region-based cluster head selection protocol in wireless sensor network." Proceedings of Integrated Intelligence Enable Networks and Computing: IIENC 2020. Springer Singapore, 202
Narayan, Vipul, and A. K. Daniel. "A novel approach for cluster head selection using trust function in WSN." Scalable Computing: Practice and Experience 22.1 (2021): 1-13.
Narayan, Vipul, and A. K. Daniel. "IOT based sensor monitoring system for smart complex and shopping malls." International conference on mobile networks and management. Cham: Springer International Publishing, 2021.
Narayan, Vipul, and A. K. Daniel. "Energy Efficient Protocol for Lifetime Prediction of Wireless Sensor Network using Multivariate Polynomial Regression Model." (2022).
Mall, Pawan Kumar, et al. "Early Warning Signs Of Parkinson’s Disease Prediction Using Machine Learning Technique." Journal of Pharmaceutical Negative Results (2022): 4784-4792.
Narayan, Vipul, et al. "FuzzyNet: Medical Image Classification based on GLCM Texture Feature." 2023 International Conference on Artificial Intelligence and Smart Communication (AISC). IEEE, 2023.
Sawhney, Rahul, et al. "A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease." Decision Analytics Journal 6 (2023): 100169.