Key result
An artificial neural network model with backpropagation was developed into an interactive Cardiac Function Prediction System using 13 clinical features to predict cardiac disease.
Why the study?
Diagnosing heart disease early is challenging for practitioners because it requires combining clinical and pathological data.
The development of a deep learning-based Cardiac Function Prediction System using 13 clinical features aims to assist medical practitioners in the early diagnosis of heart disease.
Requires prospective validation before use; leaves open whether this ANN model aids early cardiac diagnosis.
Cardiovascular Disease is the silent killer and it is one of the leading cause for global death annually. The percentage of premature death varies from 7% in high-income countries and 43% in low-income countries. It is mainly due to lifestyle changing factors such as obesity, diabetes, etc. While working to reduce earlier deaths, it is revealed, how important the earlier prediction of heart disease is. In the medical field, diagnosing heart disease earlier is a difficult task for medical practitioner since it depends on combining clinical and pathological data. The purpose of this paper is to implement a medical prediction support system for predicting cardiac disease. Deep learning approach based computational model is designed for diagnosis. This proposed system has three main steps. First, a dataset with 13 attributes (13 clinical features) from the website is collected. Second, the datasets are trained using an algorithm called artificial neural network with backpropagation technique. It can have one or more hidden layers in order to get higher accuracy. Finally, Cardiac Function Prediction System (CFPS) which is an interactive GUI is developed, where the user can enter the clinical features and get to know the current status of a patient's health. This system enhances medical care and reduces the treatment cost. This system will act as a promising tool for the medical practitioner for proper diagnosis.
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Sivaranjani et al. (2019) studied Cardiovascular Disease. Cardiac Function Prediction System (CFPS) using artificial neural network was evaluated on Prediction of cardiac disease. An artificial neural network model with backpropagation was developed into an interactive Cardiac Function Prediction System using 13 clinical features to predict cardiac disease.
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