Exploiting Neural Network for Heart Disease Probability Prediction

Abstract

In general, doctors determine the presence of heart disease through clinical evaluation and pathological data, and the diagnosis process is complex and inefficient. Based on the above situation, professionals are committed to researching efficient and accurate methods for predicting heart disease. After studying many literatures, this paper found that the existing heart disease prediction system has high requirements for clinical data. Based on the reality of the shortage of medical resources under the COVID-19 epidemic, this paper develops a simple heart disease prediction system, which predicts heart disease through simple and easy-to-measure data of patients, and then prevents heart disease. The method consists of two steps. First, collect the characteristics related to heart disease, and then select the most important 10 characteristics through correlation analysis and literature research, namely gender, age range, body mass index (BMI), smoking status, physical health index, walking difficulty, stroke status, skin cancer, diabetes, kidney disease. Second, an algorithm for heart disease based on artificial neural networks classification based on these features is developed. The prediction accuracy is close to 92%. In the future, the proposed model could be leveraged for heart disease recognition.