Heart Disease Prediction using Machine Learning Algorithms
DOI:
https://doi.org/10.48001/978-81-966500-0-1-10Keywords:
Machine Learning, Predicting Cardiovascular Attack, Feature Selection, Model OptimizationAbstract
Cardiovascular diseases rank among the primary factors leading to mortality, early detection and excellent prediction are essential. The rapid level through increased diagnostic accuracy in forecasting the occurrence of cardiac disease. Using patient information such as gender, age, and hypertension to foretell the implement of ventricular ailments, cholesterol levels, and other clinical markers, this chapter examines Robotic intelligence methods that have undergone in-depth research developed such as neural networks as a field, logic- regret, Decision-trees, and sup-vet- mac trees, have been developed. The models are trained and validated using conventional performance metrics, such as Formula One, memory, quality, and sharpness score, by utilizing a dataset from a reputable medical repository. The outputs indicate that statistical learning models, especially outfit draws near and brain organizations zeal have an eagerness to reach high prediction performance in clinical settings.
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References
Devi, R. R., Dharshini, P., Hemala, R., & Swetha, D. (2023). Heart Disease Prediction using Random Forest Classifier. 2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023. https://doi.org/10.1109/ICDSAAI59313.2023.10452459
Kee, O. T., Harun, H., Mustafa, N., Abdul Murad, N. A., Chin, S. F., Jaafar, R., & Abdullah, N. (2023). Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review. Cardiovascular Diabetology, 22(1). https://doi.org/10.1186/s12933-023-01741-7
Nagavelli, U., Samanta, D., & Chakraborty, P. (2022). Machine Learning Technology-Based Heart Disease Detection Models. Journal of Healthcare Engineering, 2022. https://doi.org/10.1155/2022/7351061
Ogunpola, A., Saeed, F., Basurra, S., Albarrak, A. M., & Qasem, S. N. (2024). Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases. Diagnostics, 14(2). https://doi.org/10.3390/diagnostics14020144
Rani, G. E., Murugeswari, R., Siengchin, S., Rajini, N., & Kumar, M. A. (2022). Quantitative assessment of particle dispersion in polymeric composites and its effect on mechanical properties. Journal of Materials Research and Technology, 19, 1836–1845. https://doi.org/10.1016/j.jmrt.2022.05.147
Shah, A., & Patel, R. (2022). Heart Disease Prediction Based on Machine Learning. International Journal for Research in Applied Science and Engineering Technology,10(8), 1027–1036. https://doi.org/10.22214/ijraset.2022.46341
Srivenkatesh, M. (2020). Prediction of Cardiovascular Disease using Machine Learning Algorithms. International Journal of Engineering and Advanced Technology, 9(3), 2404–2414. https://doi.org/10.35940/ijeat.b3986.029320
Stonier, A. A., Gorantla, R. K., & Manoj, K. (2024). Cardiac disease risk prediction using machine learning algorithms. Healthcare Technology Letters, 11(4), 213–217. https://doi.org/10.1049/htl2.12053
Uçar, M. K., Nour, M., Sindi, H., & Polat, K. (2020). The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/2836236
