Hybrid Metaheuristic–Ensemble Pipeline for Student Mental

Health: Waterwheel Plant Algorithm with Random Forest

Amel Ali Alhussan1 Abdelaziz A. Abdelhamid2

1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University,

P.O. Box 84428, Riyadh 11671, Saudi Arabia

2 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt

Received: January 17, 2026 Revised: March 03, 2026 Accepted: May 03, 2026 ⋆ Corresponding author

ABSTRACT

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.

Keywords: Depression Detection Machine Learning (ML) Waterwheel Plant Algorithm (WWPA) Random Forest

Optimization Student Mental Health Analytics

1. INTRODUCTION

Depression is considered one of the most significant issues in

the world, with millions of individuals being victims; students

are the most vulnerable group. Academic stressfulness, a feature

of academic activity, the personal, social, and emotional

issues of students precondition the development of depressive

symptoms in them [1]. Among the symptoms of depression

that are observed in students are lack of concentration, lack of

motivation and withdrawal behaviour and general poor performance

in both their academic and personal lives. The existing

stigma about mental health only exacerbates the existing problems

by compelling some learners to hesitate to seek help

[2]. In addition, conventional methods of depression testing

such as self-report surveys and clinical interviews are usually

inaccurate and time-consuming. These shortcomings indicate