Diagnosis of Huntington Disease using Fuzzy Logic Framework

Abstract

Fuzzy logic is a procedure through which disease prognostication can be feasible through fuzzy if-then rules. From the symptoms and historical data, the fuzzy system can be devised to prophesy Huntington disease as complicated expert systems exist with the help of fuzzy logic. Huntington disease is diagnosed through MRI images and CT scan, to examine any affected brain nerve due to multiple disorders in the body. But with the proposed system, it extends resemblance results to foretell Huntington disease with the symptomatic study. The usual symptoms through which Huntington disease can be diagnosed as the muscular state, mental state, cognitive state and vocal state with additional parameters in between these states. The proposed system will ascertain the healthy state of an individual as per as proffering inputs in the predefined range. The proposed system consists of 81 fuzzy rules to diagnose Huntington disease using the Mamdani model with a triangular membership function applying 4 inputs and 1 output parameter in MATLAB. The data were elicited from Punjab with the help of a questionnaire approach on 115 individuals with the proven of 20 Huntington patients. The proposed system delivers 95.65% accuracy, 85% sensitivity, and 97.89% specificity with a nominal error rate of 4.34%. So, this proposed system will be profitable for physicians to get judgments at an earliest with recommendations to diminish the painful testing and costly approach.