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Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights.

Priya Metri¹, Swetta Kukreja¹

¹Amity School of Engineering and Technology, Amity University, Mumbai, Maharashtra, India.

|July 18, 2025

Summary

Machine learning models show promise for detecting student suicidal ideation. Random Forest and SVM are common, with accuracies up to 95%, but more research is needed for diverse populations and interpretable AI.

Keywords:

Adolescent Artificial intelligence Convolutional neural network Deep learning Long and short-term memory Machine learning Scoping Review Method Suicidal Ideation Xgboost model

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Area of Science:

Psychiatry
Computer Science
Data Science

Background:

Student suicidal ideation is a significant public health concern.
Early detection is crucial for timely intervention and support.
Machine learning (ML) offers potential for identifying at-risk students.

Purpose of the Study:

To systematically review ML applications for early detection of suicidal ideation in students.
To analyze common ML algorithms, data sources, and performance metrics.
To identify limitations and future research directions in this field.

Main Methods:

Systematic review of 28 studies on ML for suicidal ideation detection.
Analysis of algorithm usage (Random Forest, SVM, deep learning).
Evaluation of model accuracy, precision, and recall based on reported results.

Main Results:

Random Forest (35%) and SVM (27%) were the most frequent algorithms.
Model accuracies varied from 70% to 95%.
Deep learning models demonstrated slightly superior precision and recall.

Conclusions:

ML techniques show potential for early detection of suicidal ideation in students.
Gaps exist in cross-cultural validation and model interpretability.
Future work should focus on hybrid and ensemble deep learning models for enhanced prediction.