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<p>Heart disease is the most dangerous and hazardous one. Human lives can be spared if the disease is diagnosed early enough and treated properly. We propose an efficient ensemble model which classifies all records correctly on the benchmark datasets. The correctness is accomplished by using Anova-Principal Component Analysis (Anv-PCA) techniques with a Stacking Classifier (SC) to select and extract the best features. The most significant component to evaluate in the medical area is recall. The findings show that the proposed Anv-PCA with SC meets all of the correctness requirements in concepts of accuracy, precision, recall, and f1-score with the highest results compared with the existing approaches. Anv-PCA, a method for selecting and extracting features, is paired with an ensemble classification algorithm in the approach we propose, which makes use of the Cleveland heart disease UCI dataset. All patient records are correctly categorized using this method, fulfilling the required criteria for correctness. The proposed model is also validated on other six publicly available benchmark datasets for diabetes, cardiovascular, Framingham, CBC, COVID-specific and HD (comprehensive) datasets available in the UCI repository, which presently meets the correctness requirements. The proposed approach exceeds all cutting-edge models.</p>

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Enhancing Disease Prediction with Correctness-Driven Ensemble Models

  • 期刊论文

  • 发表日期：

    2025年12月13日

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  • Ramdas Kapila,
  • Sumalatha Saleti

摘要

Heart disease is the most dangerous and hazardous one. Human lives can be spared if the disease is diagnosed early enough and treated properly. We propose an efficient ensemble model which classifies all records correctly on the benchmark datasets. The correctness is accomplished by using Anova-Principal Component Analysis (Anv-PCA) techniques with a Stacking Classifier (SC) to select and extract the best features. The most significant component to evaluate in the medical area is recall. The findings show that the proposed Anv-PCA with SC meets all of the correctness requirements in concepts of accuracy, precision, recall, and f1-score with the highest results compared with the existing approaches. Anv-PCA, a method for selecting and extracting features, is paired with an ensemble classification algorithm in the approach we propose, which makes use of the Cleveland heart disease UCI dataset. All patient records are correctly categorized using this method, fulfilling the required criteria for correctness. The proposed model is also validated on other six publicly available benchmark datasets for diabetes, cardiovascular, Framingham, CBC, COVID-specific and HD (comprehensive) datasets available in the UCI repository, which presently meets the correctness requirements. The proposed approach exceeds all cutting-edge models.