A Detailed Review on Mental Health Prediction Using Machine Learning

Abstract

Mental prosperity may be a major concern inside the field of healthcare. One of the most vital reasons is the require of mindfulness among the masses. It impacts the way how one considers, feels, and acts. Mental prosperity is remarkably fundamental at each organize of life, from childhood and adolescence through adulthood. Agreeing to government quantifiable information out of the entire masses of India, 130 million individuals can be persevering from a few kind of mental sickness. Wretchedness and uneasiness spiked 25 % inside the to start with year of the distant coming to (2020).All 194 WHO portion states have gotten a handle on the Comprehensive Mental Prosperity Development Organize 2013–2030 but improvement has been coordinate. This think nearly recognizes machine learning techniques and evaluates their accuracy in recognizing mental prosperity issues utilizing numerous precision criteria.

Introduction

I. Mental Health Overview

• Mental well-being is vital to emotional, psychological, and social health, affecting how individuals handle stress, make choices, and build relationships.

• Stigma and denial are major barriers—many people avoid acknowledging or seeking treatment for mental disorders.

• The COVID-19 pandemic significantly impacted mental health globally, across all demographics.

• According to the World Health Organization (WHO), the increasing complexity of modern life, competition, and changing social structures contribute to mental health issues.

Common Mental Health Disorders

1. Schizophrenia – A severe condition causing delusions, hallucinations, and cognitive disorganization.

2. Anxiety Disorders – Persistent worry and fear, including panic attacks and physical symptoms like sweating and dizziness.

3. Bipolar Disorder – Mood swings between manic (high energy, impulsiveness) and depressive episodes.

4. Post-Traumatic Stress Disorder (PTSD) – Triggered by traumatic events; symptoms include flashbacks, avoidance behavior, and strong emotional responses.

II. Machine Learning (ML) Algorithms in Mental Health

Machine learning is used to predict, classify, and analyze mental health conditions based on data patterns. Key algorithms include:

• Logistic Regression (LR) – Effective for binary classification problems.

• K-Nearest Neighbors (KNN) – Classifies based on similarity to nearby data points.

• Support Vector Machine (SVM) – Finds the best boundary between different classes of data.

• Random Forest (RF) – Ensemble of decision trees; handles overfitting well.

• Decision Tree – Simple, interpretable flowchart-like structure for predictions.

• Naïve Bayes – Based on probability; suitable for text and medical data.

III. Literature Review Highlights (2019–2023)

• Extensive research confirms the effectiveness of ML algorithms in diagnosing and predicting mental health disorders like anxiety, schizophrenia, bipolar disorder, PTSD, and autism.

• Random Forest consistently outperformed other models with up to 95% accuracy.

• Support Vector Machine (SVM) and Logistic Regression also showed high accuracy levels (91.2% and 92% respectively).

• Deep learning techniques like LSTM, CNN, and RNN are gaining popularity for their ability to capture complex patterns.

• Multi-modal machine learning (combining different data types like text, images, and voice) is an emerging trend.

• AI and hybrid systems enhance support for mental healthcare workers and improve diagnostic accuracy.

IV. Analysis of ML Algorithm Accuracy in Mental Health Prediction

• Random Forest (RF) – 95.0% accuracy

• SVM – 91.2% accuracy

• Decision Tree – 80.42%

• Logistic Regression – 79.63%

• KNN – 72.10%

Conclusion

In today\'s world mental healthis a major concern. According to WHO, mental health will be a major cause of illness within the world and individualsneed to take more care about their mental well-being for a balanced social and professional life. The purpose of this review paper is to provideinformationaboutbasic concepts of ML algorithmsregularlyutilizedwithin the mental healthdomain for their practical application.

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Copyright

Copyright © 2025 Geetika Khatri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.