Heart Disease Prediction Using Hybrid Model Integrating Artificial Neural Network, Decision Tree, and Logistic Regression

Authors

DOI:

https://doi.org/10.38124/ijsrmt.v3i11.104

Keywords:

Decision Tree, Logistic Regression, Artificial Neural Network, Hybrid Model

Abstract

Heart disease, a leading cause of global mortality, necessitates accurate prediction for timely intervention. This study proposes a hybrid model amalgamating LR, DT and ANN algorithms to enhance heart disease prediction. Using a Kaggle dataset comprising 1025 patient records with 14 features, including age, sex, chest pain, and cholesterol levels, the hybrid model achieved an impressive 88% precision. This outperforms individual models, with DT achieving 99% accuracy, LR with 80%, and ANN with 86%. Evaluation metrics demonstrate competitive performance, affirming the hybrid model as a robust tool for cardiovascular ailment prediction. The study underscores the efficacy of combining diverse algorithms, leveraging their strengths for more effective predictive modeling in cardiovascular health assessment.

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Published

2024-11-25

How to Cite

sani, nura, C Ewunonu, T., Chukwuemeka, N., I.C, E., Galadima, A., & Buba, A. (2024). Heart Disease Prediction Using Hybrid Model Integrating Artificial Neural Network, Decision Tree, and Logistic Regression. International Journal of Scientific Research and Modern Technology, 3(11), 108–115. https://doi.org/10.38124/ijsrmt.v3i11.104

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