RFAB: The Hybrid Model for the Heart Disease Prediction
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Abstract
The need for fast and accurate diagnostic systems is crucial as heart disease currently ranks as the leading cause of death worldwide. This study proposes a hybrid ensemble model called RFAB, which combines the predictions of Random Forest and Adaboost classifiers for reliable heart disease prediction. The proposed model introduces a significant improvement by utilizing an expanded dataset, increasing from 303 to 27,597 records, and applying advanced feature extraction and dimensionality reduction techniques. The testing accuracy was 95% which indicates the higher performance of the RFAB compared with SVM-81.71% and Extra Tree classifier-84%. These findings offer a non-invasive, low-cost method for early diagnosis, allowing for prompt clinical interventions and enhancing patient outcomes.
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