Prediction of Cardiovascular Disease on Transthoracic Echocardiography Data Using Artificial Neural Network - Chaithra, N.; Raviraja, S.; Sunil Kumar, S.; Ranjini , V. (Communications in Mathematics and Applications, 2024)
According to World Bank Epidemiological modelling, India has the second highest rate of Cardiovascular Disease (CVD) mortality worldwide, at 2.5 million new cases occurring annually. Heart disorder is a condition that affects heart function. One of the main problems with heart conditions in estimating a person’s risk of having insufficient blood supply to the heart. According to the World Health Statistics 2012 report, one in every three individuals in the world has high blood pressure, a condition that accounts for almost half of all fatalities from heart disease and stroke. Echocardiography is an ultrasound procedure that uses a projector to display moving images of the heart and is used to diagnose and assess a series of disorders. Authors have considered to analyse and review several recent research works on CVD and experimental models. The proposed retrospective experiment contained a total of 7304 patients Transesophageal Echocardiography (TTE) records with no missing values were chosen for the research in that 1113 patients were diagnosed with Ischemic Heart Disease (IHD) and 6191 normal patients were classified as the subject. 70% of patients' data were used to train the Neural Network and the other 30% of patients' data used to test the model. This research work estimates the efficiency of the Artificial Neural Network model to investigate the factors contributing significantly to enhancing the risk of IHD as well as accurately predict the overall risk using Machine learning software: WEKA 3.8.5. and SPSS modeler. The resulting model performance has a higher accuracy rate (97.0%) and this makes it a very vital techniques for cardiologists to screen patients at potential risk of developing the disease.
Keywords: Cardiovascular Disease; Transesophageal Echocardiography Data; Ischemic Heart disease; Artificial Neural Network