A fuzzy-based expert system to diagnose alzheimer’s disease

Abstract

Soft computing techniques came into reality to deal effectively with the emerging problems related to many fields. A medical diagnosis is totally based on human abilities, uncertain factors, ambiguous symptoms, high accuracy, and bulk of medical records. Soft computing techniques are suitable to obtain results in an efficient way in medical diagnosis. Fuzzy logic (FL) is one of the popular soft computing techniques. FL is a mathematical approach for computing and inferencing which generalizes crisp logic and sets theory employing the concept of fuzzy set. Fuzzy logic has been successfully applied in the fields of pattern recognition, image processing, knowledge engineering, medical diagnosis, control theory, etc., Alzheimer’s disease (AD) is the most popular dementia in aged people. AD is an irreversible and progressive neurodegenerative disorder that slowly destroys memory, thinking skill, and degrades of the ability of performing daily tasks. Hippocampus is a key biomarker for AD to identify the disease at an early stage. To detect and diagnose Alzheimer’s disease at an early stage, fuzzy logic is playing a vital role. In this study, computerized system for classification of AD was constructed using fuzzy logic approach, i.e., fuzzy inference system (FIS) to classify the subjects into AD, mild cognitive impairment (MCI), and normal control (non-AD) on the basis of visual features from hippocampus region.