This paper reports the findings from the experimental study of an intelligent system driven by Fuzzy Logic (FL) for depression risk diagnosis. Depression is a common psychological disorder that can cause serious health challenges if it remains undiagnosed, misdiagnosed or untreated. It represents a major public health problem identified by the world health organization (WHO) to have affected a vast majority of the productive adult population. The confusing nature of the disease symptoms makes it difficult for physicians using psychometric assessment tools alone to determine the severity of the disease. With advances in artificial intelligence (AI), intelligent computing has accelerated new approaches that can enhance medical decision support services. This paper describes research results in the development of a fuzzy driven system to determine the depression risk levels of patients. The system is implemented and simulated using MATLAB fuzzy tool box. The result of the system is consistent with an expert specialist’s opinion on evaluating the performance of the system.
Key words: Depression, depression risk, fuzzy logic, severity level, matlab, membership functions.
Copyright © 2019 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0