Machine Learning Techniques for Prediction of Mental Health

Jain, Tarun; Hada, Priyank Singh; Kumar, H. M. Keerthi; Verma, Vivek Kumar; Patni, Aayush

doi:10.1109/icirca51532.2021.9545061

Cited by 36 publications

(7 citation statements)

References 12 publications

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Section: Methodsmentioning

confidence: 99%

“…21,22 This conversion allows us to assess the performance of XGBoost and neural networks, two widely used algorithms in the mental health literature. 23,24 Lastly, a multinomial logistic regression model is developed to predict depression scores as if they were unordered categories for comparison purposes. 21 As shown in Figure 1, five-fold cross-validation is used to prevent overfitting by tuning and evaluating the models multiple times on different training and validation datasets, respectively.…”

Section: Algorithms and Model Evaluationmentioning

confidence: 99%

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Machine learning-based prediction of depression severity among medical students

Liu,

Chen,

Marrero

et al. 2023

Preprint

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Depression is a significant global cause of disability, yet it often remains undetected and untreated. Medical students have a higher likelihood of experiencing depression compared to many other age-matched groups. Previous studies have shed light on factors contributing to depression among college students. However, medical students face understudied unique experiences and demands. This gap hinders the ability to apply previously developed models to predict depression in medical students, limiting the effectiveness of mental health interventions in this population. Our study addresses this gap by presenting a machine learning model that captures the complex interplay of factors influencing the severity of depression in this population. Using data from the Healthy Minds Study, we found that a sequence of binary XGBoost models performed best at predicting the severity of depression as recorded through the Patient Health Questionnaire-9. Our modeling approach identified that prior diagnosis of depression and eating disorder, current financial stress, and younger age had the highest influence on predicted depression severity. The findings of this study hold great promise for enhancing medical education by more effectively addressing students’ depression. Our predictive model may enable educators and administrators to identify students at higher risk, facilitating timely interventions and support.

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Section: Methodsmentioning

confidence: 99%

Section: Algorithms and Model Evaluationmentioning

confidence: 99%

Machine learning-based prediction of depression severity among medical students

Liu,

Chen,

Marrero

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In [10], the author studied eight approaches: Decision Tree, Random Forest, Support Vector Machine, Naive Bayes, Logistic Regression, Extreme Gradient Boosting (XGB), Gradient Boost Machine, and Artificial Neural Network to predict whether an individual suffered from depression. Sensitivity, specificity, precision, Negative Predictive Value (NPV), F1 Score, False Negative Rate (FNR), False Positive Rate (FPR), False Discovery Rate (FDR), False Omission Rate (FOR), and accuracy are the evaluation metrics utilized.…”

Section: B Related Workmentioning

confidence: 99%

Mental Health Problems Prediction Using Machine Learning Techniques

Cheng,

Haw

2023

IJORAS

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Mental health problems encompass a range of conditions that can impact an individual's emotions and behaviors. The conventional methods of mental illness prediction often suffer from the issue of either over-detection or under-detection and the time-consuming manual review process of patients' data during screening sessions. Therefore, this research aims to utilize machine learning techniques to predict mental health problems, complementing the traditional clinical screening and diagnosis process. The proposed models in this project: Logistic Regression, K-Nearest Neighbors, and Random Forest leverage relevant factors from the dataset concerning mental health survey published by Open Source Mental Disorders in 2014 to predict mental health problems. Feature selection and hyperparameter fine-tuning are employed to identify the factors contributing to mental health problems and enhance the performance of the models. The evaluation of these models is measured using accuracy, recall, precision, F1 score, and AUROC. Experimental evaluation results indicated that the Random Forest model utilizing hyperparameters derived from the RandomizedSearchCV method outperforms during model selection using cross-validation. When predicting test set data, it exhibits a good generalization with an accuracy of 83.23%, recall of 89.87%, precision of 78.02%, F1 score of 83.53%, and AUROC of 83.57%.

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“…It usually refers to a person's mood, thoughts, and behavior [1]. One of the leading causes of suicide is poor mental health [2]. A personality disorder is one type of mental health disorder that interferes with ways of thinking, understanding situations, and relating to others.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of feature selections on some ML approaches for diagnosing the narcissistic personality disorder

Sulistiani,

Syarif,

Muludi

et al. 2024

Bulletin EEI

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Narcissistic personality disorder (NPD) is a personality disorder that affects various aspects of life, including relationships, employment, school, and finances. Persons with NPD usually feel unhappy and disappointed when no one helps them and is not praised for their achievements. Diagnosing narcissism is generally done using a screening test that consumes time and costs a lot. This research aims to evaluate the performance of several feature selection (FS) approaches on machine learning (ML) techniques (support vector machine (SVM), random forest classifier (RFC), and Naive Bayes). Three scenarios of FS (all features, the information gain technique and the gain ratio (GR) feature technique) are used for each ML method. Several experiments using the benchmark narcissistic disorder dataset have been done. It adopts the k-fold cross-validation (10-fold cross-validation) strategy. We evaluate the method’s performance by measuring its accuracy, error rate, and processing time. It is shown that the RFC GR strategy gives the best performance with an accuracy of 100%.

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Machine Learning Techniques for Prediction of Mental Health

Cited by 36 publications

References 12 publications

Machine learning-based prediction of depression severity among medical students

Machine learning-based prediction of depression severity among medical students

Mental Health Problems Prediction Using Machine Learning Techniques

Performance evaluation of feature selections on some ML approaches for diagnosing the narcissistic personality disorder

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