An Evaluation Of Artificial Neural Network In Predicting The Presence Of Heart Disease

• Main Author: Mohd Khalid, Awang
• Format: Thesis
• Language: English; English
• Published: 2000
• Subjects: QA76 Computer software
• Online Access: https://etd.uum.edu.my/181/1/MOHD_KHALID_BIN_AWANG_-_An_evaluation_of_artificial_neural_network_in_predicting_the_presence_of_hearts_disease.pdf; https://etd.uum.edu.my/181/2/1.MOHD_KHALID_BIN_AWANG_-_An_evaluation_of_artificial_neural_network_in_predicting_the_presence_of_hearts_disease.pdf; https://etd.uum.edu.my/181/

The purpose of this study is to evaluate the application of artificial neural network in predicting the presence of heart disease, particularly the angina in patients that already diagnosed with myocardial infarction. The prediction and detection of angina is important in determining the most appropriate form of treatment for these patients. Furthermore, diagnosis and management of angina is important since it can lead to the recurrent of myocardial infarction. The development of the application involves three main phases. The first phase is the development of Myocardial Infarction Management Information System (MIMIS) for data collection and management. Then followed by the second phase, which is the development of Neural Network Simulator (NNS) using back propagation for network training and testing. The final phase is the development of Prediction System (PS) for prediction on new patient’s data. All systems had been developed using Microsoft’s Visual Basics. The data used to train and test the network was provided by Alor Setar General Hospital, Kedah. The best network model produced prediction accuracy of 88.89 percents. Apart from proving the ability of neural network technology in medical diagnosis, this study also shown how the neural network could be integrated into a management information system as a prediction tools. As the pilot project, the application developed could be used as the starting point in building a medical decision support system, particularly in diagnosing the heart disease.