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.
Highlights
Intelligent computing methods for the solution of complex problem of heuristic nature are: KBS, casebased reasoning (CBR), modal-based reasoning (MBR), artificial neural network (ANN) and genetic algorithm (GA)
In this work we have developed integrated model consisting of rule-based and CBR for absolute diagnosis and integrate mode consisting of Rule-based reasoning (RBR), CBR and ANN for relative diagnosis
Global Variable: Array bin_dec[1...n], each cell stores equivalent decimal value of the binary value obtained from user input by selecting conditions; n, number of symptoms in each parameter of disease; k[ l...n], each cell contains a integer value which is equivalent to number of bits used to represent symptoms in each parameters of disease in order; Local Variable: Array UDV[l...n], stores user data vector; Input: bin[l...n] For bin_dec(i) ← convert binary value stored in bin(i) equivalent decimal value patient data vector (PDV)(i) ← bin_dec(i)/2k(j) end for 2499 | P a g e
Summary
Intelligent computing methods for the solution of complex problem of heuristic nature are: KBS (rule-based), casebased reasoning (CBR), modal-based reasoning (MBR), artificial neural network (ANN) and genetic algorithm (GA). The integration of knowledge enriched and data dominant computing methods are being extensively used days for the detection, diagnosis and treatment of many diseases in neurology and psychiatry etc. ANN-CBR combined method is used for heart transplant [28] This method described learning general knowledge in a diagnostic case-based system through the use of a neural network. Diagnosis and interpretation of different neuropsychiatric diseases based upon EEG, neuroimagin; psychological, cognitive and physiological parameters. To develop integrated model of RBR, CBR and ANN in which RBR is used to hierarchically correlate the sign and symptom of the disease and to compute cumulative confidence factor (CCF) of all the diseases for differential diagnosis. In differential diagnosis to determine the highest CCF which corresponds to sign and symptoms depicted in EEG signal, and parameters of neuroimagin, physiological, psychological and cognitive origin of a particular patient given by user.
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