A Novel Machine Learning Model for Mental Health Risk Prediction

• Author(s): Ayush T. Solanki; Dr. M. N. Nachappa
• Paper ID: 1717863
• Page: 2033-2042
• Published Date: 18-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

There is also an increased rate of mental health disorders like depression, anxiety, and stress in the modern society that has experienced the problem among persons of different ages, professions, among others. The vulnerable are especially students and young working adults under the pressure of the academic community, the doubt of career choice, the social pressure on the student, and the changes in the lifestyle. The most crucial aspect is the identification of persons at risk of developing mental disorders as soon as possible lest the disorder develops and results in severe psychological effects and a poor quality of life. Recent years have seen the fast growth of technologies based on data that opens the possibility to utilize the computational approaches to the analysis of psychological and behavioral profiles in terms of mental health.The methods of machine-learning have gained much interest in this area due to the ability to work with complex datasets and identify latent patterns that can be difficult to notice by using the standard methods of statistics. Different algorithms logistic regression, random forests, support vectors machines, and gradient boosting have been studied to predict mental health risks by using datasets which involve psychological surveys, behavioral indicators as well as demographic data. In spite of the promising outcomes of the previous research, numerous of them concentrate on a narrow set of algorithms and specific datasets or target groups of the population. Additionally, not all the studies have some standardized evaluation techniques and thus it is difficult to compare how effective various machine-learning techniques are. In order to overcome these weaknesses, the current research project suggests a comparative study of numerous machine-learning algorithms to predict mental health risks. The suggested research model will involve data mining and the identification of publicly available datasets on mental health including data collection, data pre-treatment, feature selection, model training, and evaluation of the model using commonly used performance indicators such as accuracy, precision, recall, F1-score and ROC-AUC. This study aims at defining the best machine-learning methodology used in mental health risk prediction and to offer knowledge on how data-driven models can be used to support early detection systems, support researchers and to help in the creation of intelligent mental health assessment devices.

Keywords

Mental Health Prediction, Machine Learning, Depression Detection, Anxiety Prediction, Data-Driven Models

Citations

IRE Journals:
Ayush T. Solanki, Dr. M. N. Nachappa "A Novel Machine Learning Model for Mental Health Risk Prediction" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2033-2042 https://doi.org/10.64388/IREV9I11-1717863

IEEE:
Ayush T. Solanki, Dr. M. N. Nachappa "A Novel Machine Learning Model for Mental Health Risk Prediction" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717863