Identification of Coronary Artery Disease using Artificial Neural Network and Case-Based Reasoning

Background: Cardiovascular diseases are increasing at an alarming rate with a very high rate of mortality. Coronary artery disease is one of the types of cardiovascular diseases, which is not easily diagnosed in its early stage. Prevention of coronary artery disease is possible only if it is diagnosed at an early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multilayer perceptron) with case-based reasoning to design an analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for coronary artery disease and in the second phase, if the patient is found suffering from the disease, then case-based reasoning is employed to diagnose the severity of the disease. In the first phase, a multilayer perceptron is implemented on a reduced dataset and with time-based learning for stochastic gradient descent, respectively. Results: The classification accuracy increased by 4.18 % with reduced data set using a deep neural network with time-based learning. In the second phase, when the patient was diagnosedpositive for coronary artery disease, then the case-based reasoning system was used to retrieve from the case base the most similar case to predict the severity of the disease for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners, supporting in the decisionmaking process and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.