An AI-based Decision Support System for Predicting Mental Health Disorders

Salih Tutun (), Marina E. Johnson (), Abdulaziz Ahmed (), Abdullah Albizri (), Sedat Irgil (), Ilker Yesilkaya (), Esma Nur Ucar (), Tanalp Sengun () and Antoine Harfouche ()
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Salih Tutun: Washington University in St. Louis
Marina E. Johnson: Montclair State University
Abdulaziz Ahmed: University of Alabama at Birmingham
Abdullah Albizri: Montclair State University
Sedat Irgil: Guven Private Health Laboratory
Ilker Yesilkaya: WeCureX Lab, DNB Analytics
Esma Nur Ucar: Guven Private Health Laboratory
Tanalp Sengun: WeCureX Lab, DNB Analytics
Antoine Harfouche: University Paris Nanterre

Information Systems Frontiers, 2023, vol. 25, issue 3, No 18, 1276 pages

Abstract: Abstract Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants’ answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.

Keywords: Mental Disorder; Artificial Intelligence; Machine learning; Network Science; Feature selection; SCL-90-R; Network Pattern Recognition; Healthcare Analytics; Disease Prediction; Diagnosis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10796-022-10282-5

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