INTELLIGENT COMPUTING METHODS FOR THE INTERPRETATION OF NEUROPSYCHIATRIC DISEASES BASED ON RBR-CBR-ANN INTEGRATION

Abstract

Neuropsychiatry is an integrative and collaborative field that brings together brain and behavior, but its diagnosis is complex and controversial due to the conflicting, overlapping and confusing nature of the multitude of symptoms, hence the need to retain cases in a case base and reuse effective previous solutions for current cases. This paper proposes a method based on the integration of Rule based reasoning (RBR), Case based reasoning (CBR) and Artificial neural network (ANN) that utilizes solutions to previous cases in assisting neuropsychiatrist in the diagnosis of neuropsychiatric disease. The system represents five neuropsychiatric diseases with 38 symptoms grouped into six categories. Integrated method improves the computational and reasoning efficiency of the problem-solving strategy. We have hierarchically structured the five neuropsychiatric diseases in terms of their physio-psycho (muscular, cognitive and psychological), EEG and neuroimagin based parameters. Cumulative confidence factor (CCF) is computed at different node form lowest to highest level of hierarchal structure in the process of diagnosis of the neuropsychiatric diseases. The basic objective of this work is to develop integrated model of RBR-CBR and RBR-CBR-ANN in which RBR is used to hierarchically correlate the sign and symptom of the disease and also to compute CCF of the diseases. CBR is used for diagnosing the neuropsychiatric diseases for absolute and relative diagnosis. In relative diagnosis CBR is also used to find the relative importance of sign and symptoms of a disease to other disease and ANN is used for matching process in CBR.