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- Title
Prediction of Depression Severity and Personalised Risk Factors Using Machine Learning on Multimodal Data.
- Authors
Ayodele, Adefemi; Adetunla, Adedotun; Akinlabi, Esther
- Abstract
Depression, a prevalent global mental health disorder, often leads to a reduced quality of life and an increased risk of suicide. Despite the availability of treatments, many cases go undetected, highlighting the need for an accurate machine learning (ML) prediction model for depression severity and risk factors, particularly when dealing with multimodal datasets. Previous studies that utilized ML to predict the severity of depression encountered limitations, such as small datasets and a lack of personalization. This study proposes an optimal algorithm for predicting depression severity and personalized risk factors using ML. The potential bene- fits include improved accuracy in severity assessment, personalized treatment strategies, and refined risk factor identification. The random forest (RF) algorithm emerged as the most effective, exhibiting notable performance metrics on NHANES data, including a 0.93 R-squared, 0.93 explained variance score (EVS), 0.51 mean absolute error (MAE), 1.73 mean squared error (MSE), and 1.32 root mean squared error (RMSE). Notably, RF identified both general and personalized risk factors for depression severity. This model holds promise for clinical assessment, diagnosis, and intervention planning, contributing significantly to the comprehensive management of depression.
- Subjects
MACHINE learning; MULTIMODAL user interfaces; STANDARD deviations; MENTAL illness; SUICIDE risk factors; MENTAL depression; RANDOM forest algorithms
- Publication
International Journal of Online & Biomedical Engineering, 2024, Vol 20, Issue 9, p130
- ISSN
2626-8493
- Publication type
Article
- DOI
10.3991/ijoe.v20i09.47581