Abstract
The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer’s disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.
Highlights
Mental health is a state of successful cognitive function resulting in adapting to change and coping with everyday stresses of life [1,2]
We reviewed the works focusing on the diagnosis and prediction of computer-aided diagnosis (CAD) methods proposed in the literature for suicide, neurological disorders, and mental disorders
Many classifiers were used for suicide ideation, behavior, or death prediction in the literature, including logistic regression with/without regularization [99], deep neural networks (DNNs) [104,125], decision tree algorithm [99], support vector machine (SVM) [40], random forests [104,125], Gaussian Naive Bayes (GNB) [40], extreme gradient boosting (XGB) [40], Cox regression [116], ensemble learning [117], elastic net [41], and long short-term memory convolutional neural network (LSTM-CNN) [126]
Summary
Mental health is a state of successful cognitive function resulting in adapting to change and coping with everyday stresses of life [1,2]. Mental disorders refer to a wide range of conditions affecting mood, thinking, and behavior. They could be occasional or chronic [3]. Some major mental disorders include depression, bipolar disorder (BD), and schizophrenia (SZ) [4]. Mental illnesses are globally among the leading causes of disability in Disability Adjusted Life Years (DALYs) [5]. Depressive disorders (29.74%), followed by anxiety disorders (22.86%), and schizophrenia (11.66%) are the top three contributors to mental disorder DALYs [6]
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