Background:
Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention
of Coronary Artery Disease is possible only if it is diagnosed, at 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 (multi-layer perceptron) with
Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented
on reduced dataset and with time-based learning for stochastic gradient descent respectively.
Results:
The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time
based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case
based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The
CBR model achieved 97.3% accuracy.
Conclusion:
The model can be very useful for medical practitioners as a supporting decision system and thus can save the
patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical
treatment.
Case-Based Reasoning with an Artificial Neural Network for Decision Support in Situations at Complex Technological Objects of Urban Infrastructure
