Key result
Gradient Boosting and LSTM models achieve a leading ~80% AUC in predicting cardiovascular disease risk.
Why the study?
Accurate cardiovascular disease risk prediction is essential for effective prevention, but comparative performance of traditional machine learning and deep learning models remains to be comprehensively evaluated.
Do machine learning and deep learning models accurately predict cardiovascular disease risk?
Do machine learning and deep learning models accurately predict cardiovascular disease risk?
Gradient Boosting and LSTM models demonstrate high accuracy in predicting cardiovascular disease risk, highlighting the potential of advanced machine learning in personalized medicine.
May support ML models in CVD risk research; leaves open clinical adoption without prospective validation.
Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.
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Pathan et al. (2024) studied Cardiovascular Disease. Gradient Boosting and LSTM models vs. Other traditional machine learning models (Logistic Regression, Random Forest, SVM, KNN, Decision Tree) was evaluated on Receiver Operating Characteristic Area under the Curve (ROC AUC). The Gradient Boosting Classifier and LSTM neural network demonstrated the highest performance in predicting cardiovascular disease risk, achieving ROC AUC scores of 80.25% and 80.06%, respectively.
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