Abstract
Machine learning (ML) offers promising opportunities for predicting mental health treatment needs across different age groups, thereby addressing critical challenges in early detection and intervention. This comprehensive review explores ML techniques for mental health prediction, examining supervised and unsupervised algorithms, model evaluation metrics, and age-specific challenges.
The study investigates ML applications across three primary life stages: childhood/adolescence, young adulthood, and middle/older adulthood. Each stage presents unique predictive challenges, including developmental changes, data availability, and ethical considerations. Key ML methods such as logistic regression, support vector machines, random forest, and neural networks are analysed for their effectiveness in identifying treatment needs.
Critical challenges include data limitations, model interpretability, potential algorithmic bias, and generalizability across diverse populations. The research emphasizes the importance of developing robust, diverse datasets, improving model transparency, and addressing ethical concerns in mental health prediction. While ML demonstrates significant potential in enhancing early detection, personalizing treatment, and optimizing resource allocation, the study underscores that these models should serve as clinical decision support tools, complementing rather than replacing clinical expertise. Future research should focus on developing more sophisticated, interpretable, and ethically sound ML approaches to mental health prediction.