Abstract
This study aimed to predict heart disease using machine learning models and feature selection techniques. The Cleveland Clinic Heart Disease dataset was used. Some feature selection methods were applied to identify relevant features, including Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO). Multiple machine learning models, such as Random Forest, Logistic Regression, and Support Vector Machines, were trained and evaluated for their performance in classifying heart disease. The results showed that the combination of Random Forest with RFE achieved the highest performance, exhibiting the highest accuracy (91%), precision (90%), recall (90%), and F-score (90%). Furthermore, the model with RFE and Random Forest demonstrated a superior discriminatory ability with an AUC Score of 92%. This research has implications for improving heart disease diagnosis and developing predictive models for early detection.
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Maulana, A., Faisyal, F.R., Tarmizi, F.K., Abidin, T.F., Riza, H. (2023). Optimizing Heart Disease Classification: Exploring the Impact of Feature Selection and Performance of Machine Learning Algorithms. In: Anutariya, C., Bonsangue, M.M. (eds) Data Science and Artificial Intelligence. DSAI 2023. Communications in Computer and Information Science, vol 1942. Springer, Singapore. https://doi.org/10.1007/978-981-99-7969-1_20
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DOI: https://doi.org/10.1007/978-981-99-7969-1_20
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