Aim: Heart diseases (HD) refer to many diseases such as coronary heart disease, heart failure, and heart attack. Every year, approximately 647.000 people die in the United States (U.S.) from HD. Genetic and environmental risk factors have been identified due to numerous studies to determine HD risk factors.Material and Method: In this study, the Multilayer Perceptron (MLP) model was constructed to predict the risk factors related to HD in both genders. The relevant dataset consisted of 270 individuals, 13 predictors, and one response/target variable. Model performance was evaluated using overall accuracy, the area under the ROC (Receiver Operating Characteristics) curve (AUC), sensitivity, and specificity metrics.Results: The performance metric values for accuracy, AUC, sensitivity and specificity were obtained with 95% CI, 0.876 (0.79-0.937), 0.935 (0.877-0.992), 0.921 (0.786-0.983) and 0.843 (0.714-0.93), respectively. According to the relevant model findings, blood pressure, the number of significant vessels coloured by fluoroscopy, and cholesterol variables were the three most crucial HD classification factors.Discussion: It can be said that the model used in the present study offers an acceptable estimation performance when all performance metrics are considered. In addition, when compared with the studies in the literature from both data science and statistical point of view, it can be stated that the findings in the current study are more satisfactory.Conclusion: Due to the predictive performance in this study, the MLP model can be recommended to clinicians as a clinical decision support system. Finally, we propose solutions and future research pathways for the various computational materials science challenges for early HD diagnosis.