Why the study?
Cardiovascular diseases are a leading cause of death globally, creating a pressing need to evaluate innovative machine learning methodologies for early diagnosis.
Which machine learning model provides the best classification accuracy and practicality for the early diagnosis of cardiovascular disease?
Population
Heart disease dataset evaluated across 14 features without missing data
Comparison
SVM vs logistic regression vs DT vs ANN
Design
Comparative machine learning model evaluation study
Key result
Logistic regression achieved the highest mean classification accuracy of 93.18% for cardiovascular disease detection, while decision trees provided the highest precision and F1-score at 95.3%.
Authors
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Hypothesis-generating for ML-based CVD detection; leaves open prospective validation before clinical use.
Which machine learning model provides the best classification accuracy and practicality for the early diagnosis of cardiovascular disease?
Basic machine learning models demonstrate similar accuracy for cardiovascular disease classification, with decision trees offering the greatest practicality for clinical use due to their interpretability.
Chalapathi et al. (2024) studied Cardiovascular disease (n=297). Machine learning models (Logistic Regression, Decision Tree, ANN, SVM) vs. Comparison among models was evaluated on Classification accuracy. Logistic regression achieved the highest mean classification accuracy of 93.18% for cardiovascular disease detection, while decision trees provided the highest precision and F1-score at 95.3%.
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