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Predicting Heart Diseases Using Machine Learning and Different Data Classification Techniques

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Achieving 100% accuracy in predicting heart disease emphasizes the effectiveness of enhanced ensemble learning methods.
Model performance was significantly improved using feature selection techniques like ANOVA FS, Chi2 FS, and MI FS.
Data imbalance was addressed with Synthetic Minority Oversampling Technique, enabling more robust model training.
Utilizing advanced machine learning techniques supports early diagnosis and potentially improves patient outcomes.

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Analysis of heart disease outcomes using machine learning and feature selection techniques, highlighting predictive capabilities.

Mrs.Deepa et al. (2025) studied this question.

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