A hybrid deep neural net learning model for predicting Coronary Heart Disease using Randomized Search Cross-Validation Optimization

Abstract

Coronary Heart Disease (CHD) is a life-threatening public health problem. Many chronic CHDs and health risks can be avoided, reversed, and reduced with proper risk assessment. Medical professionals find it challenging to anticipate heart attacks and heart failures since it is a complex process requiring knowledge, experience, and medical resource facilitation. Although healthcare is generally information-savvy, not all available data are analysed to find hidden patterns and make informed and timely decisions since heart disease prediction relies heavily on clinical data processing. This study proposes a hybrid deep neural net learning model for predicting CHD using the BRFSS-2015 Dataset. The best features subset is chosen based on the co-relation score and dataset classes are balanced using the cluster-abundant data class approach. Bi-direction Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) hyper-parameter tuning is accomplished using Randomized Search Cross-Validation Optimization (RSCV). In comparison to GRU, LSTM, and BiLSTM-GRU, this suggested model obtains a classification accuracy of 98.28% which outperforms existing models.