Explainable Ensemble Learning Models for Early Detection of Heart Disease

Raed Hassan Laftah, Karim Hashim Kraidi Al-Saedi

Abstract


Coronary diseases (CVD) are a major global health concern, and timely, accurate diagnosis is crucial for effective treatment and management. As machine learning is, has steadily been on the improvement way, and it's there where we find the transformative potential for enhancing the diagnostic accuracy for their predictive accuracy using the Local Interpretable Model-agnostic Explanations technique to ensure the explainability of our models. With the advancement of machine learning, we aim to enhance diagnostic accuracy by developing a high-precision prediction tool for heart disease using various ML models. We utilized a Kaggle dataset to implement several ML models, including Random Forest, Gradient Boosting, CatBoost, K-Nearest Neighbor, Naive Bayes, Support Vector Machine, and AdaBoost, with appropriate data preprocessing. The soft voting ensemble method, combining various models, achieved a notable 98.54% accuracy and 99% precision, recall, and f1-score, with Random Forest, CatBoost, and the Voting Classifier outperforming others. These results indicate that our model is highly reliable and sets a new standard for CVD prediction. Future research should focus on validating this model with larger datasets and exploring deep learning approaches.

Keywords


Cardiovascular Disease Prediction; Machine Learning; Ensemble Learning; Soft Voting Classifier.

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References


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DOI: https://doi.org/10.18196/jrc.v5i5.22448

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