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Nowadays, the necessity for precise early identification of cardiac conditions is critical with the conventional methods. This paper assesses the efficacy of various machine learning algorithms by analyzing a vast dataset of cardiovascular health indicators. The methods utilized include Logistic Regression (LR), K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Stochastic Gradient Descent (SGD), and AdaBoost (ADA) classifiers. Finally, evaluate these algorithms using several performance indicators such as F1-score, ROC curves, DET curves, recollection, recall, accuracy, and precision. The findings showed that the Random Forest and Decision Tree classifiers are the best, each attaining an accuracy of 98.54%. This comparative analysis focal points the superiority of ensemble learning techniques in making robust predictions, thereby significantly contributing to the early detection and management of heart disease.

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Ensemble Machine Learning Approaches for Accurate Early Detection of Heart Disease

  • 会议论文

  • 发表日期：

    2026年

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  • K. Padma Priya,
  • S. Arunachalam,
  • R. Amuthan,
  • Somasundaram Kasiviswanathan

摘要

Nowadays, the necessity for precise early identification of cardiac conditions is critical with the conventional methods. This paper assesses the efficacy of various machine learning algorithms by analyzing a vast dataset of cardiovascular health indicators. The methods utilized include Logistic Regression (LR), K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Stochastic Gradient Descent (SGD), and AdaBoost (ADA) classifiers. Finally, evaluate these algorithms using several performance indicators such as F1-score, ROC curves, DET curves, recollection, recall, accuracy, and precision. The findings showed that the Random Forest and Decision Tree classifiers are the best, each attaining an accuracy of 98.54%. This comparative analysis focal points the superiority of ensemble learning techniques in making robust predictions, thereby significantly contributing to the early detection and management of heart disease.