WT-CNN: A Hybrid Machine Learning Model for Heart Disease Prediction

Abstract

Heart disease remains a predominant health challenge, being the leading cause of death worldwide. According to the World Health Organization (WHO), cardiovascular diseases (CVDs) take an estimated 17.9 million lives each year, accounting for 32% of all global deaths. Thus, there is a global health concern necessitating accurate prediction models for timely intervention. Several data mining techniques are used by researchers to help healthcare professionals to predict heart disease. However, the traditional machine learning models for predicting heart disease often struggle with handling imbalanced datasets. Moreover, when prediction is on the bases of complex data like ECG, feature extraction and selecting the most pertinent features that accurately represent the underlying pathophysiological conditions without succumbing to overfitting is also a challenge. In this paper, a continuous wavelet transformation and convolutional neural network-based hybrid model abbreviated as WT-CNN is proposed. The key phases of WT-CNN are ECG data collection, preprocessing, RUSBoost-based data balancing, CWT-based feature extraction, and CNN-based final prediction. Through extensive experimentation and evaluation, the proposed model achieves an exceptional accuracy of 97.2% in predicting heart disease. The experimental results show that the approach improves classification accuracy compared to other classification approaches and that the presented model can be successfully used by healthcare professionals for predicting heart disease. Furthermore, this work can have a potential impact on improving heart disease prediction and ultimately enhancing patient lifestyle.