Volume 6 • Issue 1 • PP: 40–54 • 2026
Hybrid Metaheuristic–Ensemble Pipeline for Student Mental Health: Waterwheel Plant Algorithm with Random Forest
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The problem of depression among college students has become a burning research problem, as the number of psychosocial stress factors, academic loads, and lifestyle disorders that lead to the worsening of mental health has increased. Driven by the increasing need for innovative, data-driven, and interpretable diagnostic models, this paper presents a combined Machine Learning (ML) and metaheuristic optimization model for predicting depression using multidimensional psychosocial and academic data collected from 100 Computer Science students. The suggested hybrid model combines a Random Forest (RF) classifier with the Waterwheel Plant Algorithm (WWPA), a nature-inspired mechanism for optimizing hyperparameter settings and feature selection. Experimentation using the Random Forest baseline model yielded a baseline accuracy of 0.9081, a Sensitivity (True Positive Rate) of 0.8936, and an F-Score of 0.9032. The hybrid WWPA+Random Forest model showed significant gains after introducing the WWPA optimization method, achieving a high accuracy of 0.9577, a sensitivity of 0.9502, a specificity of 0.9644, and an F-score of 0.9553. These findings validate the high-quality performance of the proposed model in achieving balanced, high-precision classification and in resisting overfitting. The results highlight the potential to integrate ensemble learning with bio-inspired optimization to advance depression prediction, providing a scalable, explainable, and ethically appropriate framework for predicting depression early in a person’s life. This work opens the way to creating a proactive digital mental health system that will enable educational organizations to identify at-risk students early and offer timely, individualized support for well-being and academic achievement.
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