Emerging Role of Intelligent Techniques for Effective Detection and Prediction of Mental Disorders

Jain, Priti Rai; Quadri, S. M. K.

doi:10.1007/978-981-15-9509-7_16

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Using ML, these illnesses can be identified at an earlier or prodromal stage when interventions may be more effective, and personalized treatments based on an individual's unique characteristics can be used. Although in the medical and health fields lots of algorithms have been developed and presented, review papers have been presented for application of AI in mental health and common trends, gaps and challenges have been highlighted [17][18][19][20][21].…”

Section: Related Work and Risk Factor Analysismentioning

confidence: 99%

Analysis of Risk Factors and Diagnosis for Anxiety Disorder in Older People with the Aid of Artificial Intelligence: Observational Study

Wang,

Black,

Rankin

et al. 2023

2023 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS)

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Anxiety disorders are the most common mental health problems particularly in older people who suffer from loneliness and social isolation, chronic health conditions, financial insecurity and other factors that can lead to anxiety disorders. The high prevalence and health risks of anxiety disorders, and the requirement for effective mental healthcare, integrated with recent advances in artificial intelligence, has resulted in an increase in the exploration of how machine learning can aid the diagnosis and prediction of mental health problems.Data from the Trinity-Ulster-Department of Agriculture (TUDA) study will be utilized to identify risk factors for anxiety in community dwelling older adults using machine learning techniques. The TUDA study includes detailed information on sociodemographic, clinical, biochemical, nutritional, and lifestyle factors in 5186 older people recruited from the Republic of Ireland and Northern Ireland. These characteristics could foster the prediction of anxiety disorders using supervised machine learning methods. Biomarker risk factor analysis was conducted to facilitate feature engineering. In this observational study, several classical machine learning models have been trained to predict anxiety disorders. Principal component analysis was used for further feature reduction, comparing the accuracy results of various features, and determining the impact of features on the predictions of each method. The models' performance was assessed on a held-out test set and achieved an accuracy of 85.4% (sensitivity: 67.0%, specificity: 90.3%) and 83.4% (sensitivity: 81.5%, specificity: 83.9%) for two best performing methods i.e., random forest and support vector machine respectively, using the standard Synthetic Minority Oversampling Technique. Risk factors such as female sex, loneliness, separated/divorced conditions, lifestyle-related, socio-economic low status, chronic diseases and family related diseases are identified. These results will aid in the early detection of anxiety disorder in future studies.

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