Development and Performance Evaluation of a Heart Disease Prediction Model Using Convolutional Neural Network

Heart disease is a leading cause of mortality globally and its prevalence is increasing year after year. Recent statistics fromthe World Health Organization show that about 17.9 million individuals are embattled with heart diseases annually and people underthe age of 70 account for one-third of these deaths. Hence, there is need to intensify research on early heart disease prediction andartificial intelligence-based heart disease prediction systems. Previous heart disease prediction systems using machine learningtechniques are unable to manage large amount of data, resulting in poor prediction accuracy. Hence, this research employsConvolutional Neural Networks, a deep learning approach for prediction of heart diseases. The dataset for training and testing themodel was obtained from a government owned hospital in Nigeria and Kaggle. The resulting system was evaluated using precision,recall, f1-score and accuracy metrics. The results obtained are: 0.94, 0.95, 0.95 and 0.95 for precision, recall, f1-score and accuracyrespectively. This show that the CNN-based model responded very well to the prediction of heart diseases for both negative and positiveclasses. The results obtained were also compared to some selected machine-learning models like Random Forest, Naïve Bayes, KNNand Logistic Regression and results show that the developed model achieved a significant improvement over the methods considered.Therefore, convolutional neural network is more suitable for heart disease prediction than some state-of-the-art machine-learningmodels. The contribution to knowledge of this research is the use of Afrocentric dataset for heart disease prediction. Future researchshould consider increasing the data size for model training to achieve improved accuracy