MindScan: Mental Disorder Diagnosis using Machine Learning

This paper proposes a machine learning-based system for early detection and assessment of mental health issues, enabling timely intervention and support. Moreover, it contributes to reducing the stigma associated with seeking help for psychological concerns by offering a private and non-judgmental digital assessment approach. The paper outlines the system's features, including an intuitive UI, standardized quizzes for various disorders, subjective questions with audio-based answers for enhanced diagnostic accuracy, and an "Attention Monitoring Game" tailored for individuals with ADHD. It integrates counseling resources, information on nearby clinics, and envisions future enhancements such as expanding disorder coverage, improving model accuracy, incorporating hardware-based assessments, and introducing diverse coping strategies. The paper contributes to the advancement in mental health technology, emphasizing the potential of machine learning to revolutionize mental health care and accessibility.

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Publication Details

Published
2024-09-20
DOI
https://doi.org/10.1109/i3ceet61722.2024.10994115
Citations
1
Primary Topic
Artificial Intelligence in Education
Type
article
Field-Weighted Citation Impact
0.78
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article

MindScan: Mental Disorder Diagnosis using Machine Learning

Kiran Gawande, Tisha Negandhi, Purval Dhumale, Mishika Vachhani
1 citations
Artificial Intelligence in Education
0.78
article

MindScan: Mental Disorder Diagnosis using Machine Learning

Kiran Gawande, Tisha Negandhi, Purval Dhumale, Mishika Vachhani
article en
1 citations

Abstract

This paper proposes a machine learning-based system for early detection and assessment of mental health issues, enabling timely intervention and support. Moreover, it contributes to reducing the stigma associated with seeking help for psychological concerns by offering a private and non-judgmental digital assessment approach. The paper outlines the system's features, including an intuitive UI, standardized quizzes for various disorders, subjective questions with audio-based answers for enhanced diagnostic accuracy, and an "Attention Monitoring Game" tailored for individuals with ADHD. It integrates counseling resources, information on nearby clinics, and envisions future enhancements such as expanding disorder coverage, improving model accuracy, incorporating hardware-based assessments, and introducing diverse coping strategies. The paper contributes to the advancement in mental health technology, emphasizing the potential of machine learning to revolutionize mental health care and accessibility.

Openalex Percentile: Top 16%
Artificial Intelligence in Education
0.78
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