Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A...

Area of Science:

• Computational intelligence
• Psychiatry
• Data mining

Background:

• Psychological disorders are increasingly prevalent, impacting mental and brain function.
• Accurate diagnosis is crucial for effective treatment and management.
• Existing diagnostic methods can be enhanced through advanced computational approaches.

Purpose of the Study:

• To comprehensively review computational techniques for diagnosing major psychological disorders.
• To examine the application of supervised learning and nature-inspired computing in this field.
• To identify research gaps and future directions for computational diagnosis of psychological disorders.

Main Methods:

• Systematic literature review methodology.
• Analysis of supervised learning and nature-inspired computing techniques.
• Exploration of datasets, feature selection, and accuracy metrics.

Main Results:

• Supervised and nature-inspired computing techniques show significant promise in diagnosing psychological disorders.
• Feature selection positively impacts diagnostic accuracy.
• Research gaps exist, particularly for diagnosing mania, insomnia, and mood disorders using novel computational methods.

Conclusions:

• Nature-inspired computing offers a promising avenue for advancing the diagnosis of psychological disorders.
• Further research is needed into binary or chaotic variants of these techniques for enhanced diagnostic capabilities.
• This review serves as a guide for future research in computational diagnosis of mental health conditions.