A study of mobile CDSS for cardiovascular disease diagnosis

Although cardiovascular diseases are the number one cause of death globally, they are often diagnosed in hospitals during the late stages of life. This paper aims to make a cardiovascular disease diagnose system in the mobile environment using the Artificial Neural Network. Survey data has been collected from public institutions based on characteristics including, gender type, age, height, weight, body mass index, high blood glucose, heart rates, end-systolic and end-diastolic pressure, history of cardiac infarction and angina pectoris. The collected data is manipulated through training functions. The training functions are compared using Bayesian Regulation backpropagation and Levenberg-Marquardt backpropagation. Subsequently, the computed results are analysed which show significant performance. Finally, the results are analysed by using performance functions: mean squared error and sum squared error. Consequently, this study validates the accuracy of cardiovascular disease diagnosis by comparing with error rates, trained results, and the actual data.