Predicting Heart Disease through Machine Learning Methods

Latthika S1

1

Publication Date: 2024/09/24

Abstract: Heart diseases including heart attacks, cause about 31% of global deaths, remaining a significant health threat despite preventability. Limited tech advancements and awareness, especially in developing nations, amplify this challenge. Machine learning offers promise in tackling this issue, with studies advocating ensemble methods for accurate predictive models. These models analyze extensive medical data to efficiently predict heart diseases, undergoing stages like data exploration, feature selection, model implementation, and comparative analysis. A model using Logistic Regression, Naive Bayes, and Random Forest initially identified top-performing models, later refined to CatBoost, RandomForest, and XGBoost through cross-validation and tuning. A hybrid model, combining Logistic Regression, CatBoost, and RandomForest, achieved a 97% accuracy, showcasing improved precision, recall, F1 score, and ROC AUC. This underscores machine learning's potential in enhancing predictive accuracy and refining strategies to combat heart diseases effectively.

Keywords: Logistic Regression(LR), K-Nearest Neighbors(KNN), RandomForest(RF), CatBoost(CB), XSBoost (XSB), Stochastic Gradient Descent(SGD), Cross- Validation(CV), Support Vector Machine(SVM) Hyperparameter Tuning(HT) and Voting Classifier(VC).

DOI: https://doi.org/10.38124/ijisrt/IJISRT24SEP382

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24SEP382.pdf

REFERENCES

    1. Ahmed, H., Younis, E. M., Hendawi, A., & Ali, A. A. (2020). Heart disease identification from patients’ social posts, machine learning solution on Spark. Future Generation Computer Systems, 111, 714-722
    2. Ali, M. M., Paul, B. K., Ahmed, K., Bui, F. M., Quinn, J. M., & Moni, M. A. (2021). Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Computers in Biology and Medicine, 136, 104672
    3. Bhushan, M., Pandit, A., & Garg, A. (2023). Machine learning and deep learning techniques for the analysis of heart disease: a systematic literature review, open challenges and future directions. Artificial Intelligence Review, 1-52
    4. Chang, V., Bhavani, V. R., Xu, A. Q., & Hossain, M. A. (2022). An artificial intelligence model for heart disease detection using machine learning algorithms. Healthcare Analytics, 2, 100016
    5. Diwakar, M., Tripathi, A., Joshi, K., Memoria, M., & Singh, P. (2021). Latest trends on heart disease prediction using machine learning and image fusion. Materials Today: Proceedings, 37, 3213-3218
    6. Jinny, S. V., & Mate, Y. V. (2021). Early prediction model for coronary heart disease using genetic algorithms, hyper-parameter optimization and machine learning techniques. Health and Technology, 11, 63-73
    7. Katarya, R., & Meena, S. K. (2021). Machine learning techniques for heart disease prediction: a comparative study and analysis. Health and Technology, 11, 87-9
    8. Malakouti, S. M. (2023). Heart disease classification based on ECG using machine learning models. Biomedical Signal Processing and Control, 84, 104796.
    1. Naseri, A., Tax, D., van der Harst, P., Reinders, M., & van der Bilt, I. (2023). Data-efficient machine learning methods in the ME-TIME study: Rationale and design of a longitudinal study to detect atrial fibrillation and heart failure from wearables. Cardiovascular Digital Health Journal
    2. Pires, I. M., Marques, G., Garcia, N. M., & Ponciano, V. (2020). Machine learning for the evaluation of the presence of heart disease. Procedia Computer Science, 177, 432-437.
    3. Rimal, Y., Paudel, S., Sharma, N., & Alsadoon, A. (2023). Machine learning model matters its accuracy: a comparative study of ensemble learning and AutoML using heart disease prediction. Multimedia Tools and Applications, 1-18