Hybrid Machine Learning Model for Risk Prediction and Action Recommendation Based on Artificial Mental Systems

Hadi Asnal; Khusaeri Andesa; Fitry Erlin; Junadhi

doi:10.34148/teknika.v14i3.1357

Authors

Hadi Asnal Universitas Sains dan Teknologi Indonesia, Pekanbaru, Riau, Indonesia
Khusaeri Andesa Universitas Sains dan Teknologi Indonesia, Pekanbaru, Riau, Indonesia
Fitry Erlin Universitas Sains dan Teknologi Indonesia, Pekanbaru, Riau, Indonesia
Junadhi Universitas Sains dan Teknologi Indonesia, Pekanbaru, Riau, Indonesia

DOI:

https://doi.org/10.34148/teknika.v14i3.1357

Keywords:

Mental Health, Machine Learning, Risk Classification, Hybrid Model, Mobile Application

Abstract

Mental health problems are increasingly prevalent among the younger generation, particularly those active on social media, yet early detection efforts often remain limited. Previous studies have explored text-based approaches for identifying mental health issues, but many are constrained by low accuracy in differentiating multiple psychological states or lack integration into accessible tools for end-users. This study addresses these gaps by proposing a hybrid machine learning model for early detection of mental health conditions through social media text analysis. Five algorithms were evaluated, and a soft voting ensemble combining Logistic Regression and Support Vector Machine (SVM) was developed to improve classification across five mental states (Anxiety, Depression, Stress, Emotional Exhaustion, and Healthy) and three risk levels (Low, Medium, High). To ensure practical utility, the model was deployed in an Android-based application, SmartRisk, which allows users to input free text and receive automated assessments. The findings show that the proposed hybrid approach significantly improves detection performance, particularly in identifying depression and high-risk cases, while maintaining high usability in real-world application. The novelty of this study lies in combining hybrid ensemble learning with mobile deployment for practical, text-based early detection of mental health, offering both methodological advancement and societal impact.

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