International Journal of Science, Engineering and TechnologyAuthors: Rohit Rana, Dr. Raj Kumar
Abstract: Background: Although the prevalence of mental health conditions like depression, anxiety, and chronic stress is on the rise among students and young adults globally, traditional clinical assessment primarily relies on self-report questionnaires and scheduled clinician contact, which can cause delays in identifying at-risk individuals. In order to support early mental health risk screening, this paper proposes IMHAP-Net, a layered, explainable machine learning framework that combines heterogeneous behavioural, academic, and self-report data with a stacked ensemble of tree-based and deep learning models. Each prediction is traceable to interpretable contributing factors. Data collection, pre-processing, feature engineering, a hybrid stacking ensemble (Random Forest, XGBoost, LightGBM, CatBoost, and a deep neural network combined via a meta-learner), an explainable-AI layer (SHAP/LIME), and a risk-assessment layer that transforms model outputs into a continuous Mental Health Risk Index (MHRI) and a four-level categorical risk label comprise the framework's six layers. To be finished following trials on the target dataset or datasets; see to Section 20 for the runnable implementation and Section 12 for the metric template.] The contributions include: (i) an open implementation path (Colab/PyTorch/Scikit-learn) that generates auditable, explainable outputs instead of a black-box label; (ii) a formally defined Mental Health Risk Index combining multiple sub-scale predictions; (iii) a stacking-ensemble formulation with explicit meta-learner equations; and (iv) a reusable six-layer architecture that separates prediction from explanation and risk scoring.
