The random forest classifier achieved the highest accuracy of 0.91 for predicting cardiovascular disease, outperforming XGBoost, decision tree, logistic regression, and deep learning models.
Do machine learning models, specifically random forest classifiers, accurately predict cardiovascular diseases?
Machine learning models, particularly random forest classifiers, demonstrate high accuracy (0.91) in predicting cardiovascular diseases, highlighting their potential utility in early diagnosis and management.
Developing a predictive model for detecting cardiovascular diseases (CVDs) is crucial due to its high global fatality rate. With the advancements in artificial intelligence, the availability of large-scale data, and increased access to computational capability, it is feasible to create robust models that can detect CVDs with high precision. This study aims to provide a promising method for early diagnosis by employing various machine learning and deep learning techniques, including logistic regression, decision trees, random forest classifier, extreme gradient boosting (XGBoost), and a sequential model from Keras. Our evaluation identifies the random forest classifier as the most effective model, achieving an accuracy of 0.91, surpassing other machine learning and deep learning approaches. Close behind are XGBoost (accuracy: 0.90), decision tree (accuracy: 0.86), and logistic regression (accuracy: 0.70). Additionally, our deep learning sequential model demonstrates promising classification performance, with an accuracy of 0.80 and a loss of 0.425 on the validation set. These findings underscore the potential of machine learning and deep learning methodologies in advancing cardiovascular disease prediction and management strategies.
Gaire et al. (Mon,) conducted a other in Cardiovascular disease (n=308,854). Random Forest Classifier vs. Logistic regression, decision tree, XGBoost, and deep learning sequential models was evaluated on Classification accuracy for predicting cardiovascular disease. The random forest classifier achieved the highest accuracy of 0.91 for predicting cardiovascular disease, outperforming XGBoost, decision tree, logistic regression, and deep learning models.