Heart Disease Detection and Classification Using Ensemble Technique

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Priyanshi Agrawal; Akshara Sharma; Priyanshi Rajawat; Yasha Istwal

Heart Disease Detection and Classification Using Ensemble Technique

Authors: Priyanshi Agrawal, Akshara Sharma, Priyanshi Rajawat, Yasha Istwal

Unique Paper ID: 181677
Volume: 12
Issue: 1
PageNo: 5006-5014

Keywords: No Keywords Found

Abstract:
cardiovascular diseases (CVD) account for a substantial proportion of deaths worldwide, underlining the necessity of accurate diagnostic methods for early intervention. This study offers a ML framework for CVD prediction, using an openly accessible data set comprising clinical and demographic data. The process involves refining the dataset, identifying important predictors, and benchmarking several classification techniques such as tree-based approaches like Random Forest and advanced boosting techniques such as Gradient Boosting (GB) and XGBoost (XGB). A soft-voting ensemble model was implemented to enhance prediction accuracy, with performance evaluated using metrics like measures of correctness (accuracy), relevance (precision), completeness (recall), harmonic mean (F1 score), and discriminative ability (ROC-AUC). The results demonstrate the potential of ensemble techniques in improving diagnostic efficiency, paving the way for integration into clinical decision-making systems.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 1
PageNo: 5006-5014

Heart Disease Detection and Classification Using Ensemble Technique

Available:https://ijirt.org/article?manuscript=181677

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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