Cutting-Edge Neural Network for Early Cardiovascular Disease Prevention
Keywords:
Cardiovascular diseases, Machine Learning, Deep Neural Network, Prediction, PreventionAbstract
Cardiovascular diseases (CVD) are common and life-threatening, requiring early detection to reduce mortality. This study presents an efficient system for predicting and preventing CVD. It utilizes a hybrid dataset from various sources datasets, preprocesses them. Feature selection methods like ANOVA and CHI2 enhance prediction accuracy. The Class Balanced Feature Selection Deep Neural Network model is an enhanced deep neural network that incorporates balanced data and utilizes a prominent feature selection technique. Multiple classifiers, including Naïve Bayes, Decision Tree, CBFS-DNN, SVM, Random Forest, and KNN are trained on the hybrid dataset using the selection of features and class balancing. The DNN with CHI2 selection achieves an impressive 99.79% accuracy, demonstrating high Precision, Recall, and 97.3%, 97.2%, and 97.2%, respectively, for the F1 Score.
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