Predictive Modeling of Mental Health Conditions Using Machine Learning Algorithms

Narendra Kumar Dewangan¹, Abhishek Anand², Gunjan Chhabra³, Nitin Ajabrao Dhawas⁴,

Imran Ibrahim Sayyad⁵

¹Asst. Professor, Department of AIML, SSIPMT Raipur India.

²Department of Pharmacy Practice, Teerthanker Mahaveer College of Pharmacy, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India -244001.

³Dept of Computer Science and Engineering, School of Science and Technology,

Swami Rama Himalayan University, Dehradun, Uttarakhand, India.

⁴Department of Electronics and Telecommunication Engineering,

Nutan Maharashtra Institute of Engineering and Technology, Pune Pin 410507, Maharashtra, India.

⁵Department of Mechanical Engineering, Assistant Professor, Sanjivani College of Engineering,

Affiliated to Savitribai Phule Pune University, Pune. Maharashtra, India.

*Corresponding Author E-mail: narendra.nic@gmail.com

ABSTRACT:

Millions of people of all ages and socioeconomic backgrounds are impacted by mental health illnesses, which are becoming a major global concern. Early detection and intervention are crucial in managing these conditions effectively. In generic diagnostic procedure often rely on subjective assessments and clinical interviews, which may delay diagnosis and treatment, In machine learning (ML), there is increasing potential to invent the predictive procedure that can recognize individuals at cause of mental health issues using behavioral, physiological, and demographic data. A thorough investigation into the use of machine learning methods for the early detection of mental health conditions such bipolar disorder, anxiety, and depression is presented in this research report. A systematic literature review is conducted to evaluate existing models and datasets. To improve prediction accuracy, a unique approach that combines ensemble learning, feature selection, and data preparation is put forth. Real-world datasets sourced from public repositories are used to assess the performance of several machine learning methods, such as Random Forest, Support Vector Machine (SVM), Logistic Regression, and Gradient Boosting. The findings show that ensemble approaches—in particular, XGBoost—perform better, achieving 94.3% accuracy, 92.7% sensitivity, and 95.1% specificity. The results highlight how machine learning might help with early diagnosis and individualized mental health treatment. This study offers a foundation for further research and therapeutic applications while also adding to the expanding corpus of knowledge in computational psychiatry.

KEYWORDS: Machine Learning, Support Vector Machine, XGBoost, Gradient Boosting.

1. INTRODUCTION:

A major contributor to the global burden of disease, mental health disorders rank among the primary causes of disability globally. The World Health Organization (WHO, 2022) estimates that over 970 million people worldwide suffer from mental health issues, with anxiety and depression being the most common. Anxiety disorders affect over 301 million people, including 58 million children and adolescents, while major depressive disorder (MDD) alone affects over 280 million people. In addition to being crippling for people, these illnesses have a significant financial and social impact on society and healthcare systems. According to the Global Burden of Disease Study, mental and substance use disorders are the second most common cause of disability after musculoskeletal illnesses, accounting for 16% of all disability-adjusted life years (DALYs) worldwide, Because of widespread stigma, limited access to mental health services, and systemic underfunding of mental health infrastructure, a sizable fraction of affected individuals remain undiagnosed and untreated despite the high prevalence and profound impact¹. Stigma remains one of the most formidable barriers to seeking help for mental health issues. Cultural misconceptions, fear of discrimination, and internalized shame often deter individuals from disclosing symptoms or accessing professional care². In low- and middle-income countries (LMICs), the treatment gap is especially severe, with up to 75% of individuals with mental disorders receiving no treatment (WHO, 2021). Even in high-income countries, long wait times, high costs, and a shortage of trained mental health professionals limit timely access to care³. These challenges are further exacerbated by the chronic underfunding of mental health services, which receive less than 2% of national health budgets in most countries. As a result, many individuals suffer in silence, and their conditions often worsen before receiving intervention, Traditional psychiatric evaluation relies heavily on clinical interviews, Self-reported inquiries (e.g., PHQ-9, GAD-7), and observational assessments, which are inherently subjective and time-consuming⁴. Patients' capacity to precisely remember and describe their symptoms is frequently relied upon by clinicians, yet this capacity may be weakened during periods of extreme anxiety or despair. Additionally, the International Classification of Diseases (ICD-11) and Diagnostic and Statistical Manual of Mental Disorders (DSM-5) use diagnostic criteria based on symptom clusters rather than biological markers, which may result in heterogeneity in clinical presentations and misdiagnosis⁵. These methods are also resource-intensive, requiring trained professionals and face-to-face interactions, which limits scalability—especially in rural or underserved areas. Consequently, diagnostic delays are common, with individuals often waiting months or even years before receiving appropriate treatment⁶. This delay not only worsens prognosis but also increases the risk of comorbidities, suicidal ideation, and functional impairment⁷. Combining machine learning (ML) and artificial intelligence (AI) into mental health diagnostics presents a transformative opportunity to overcome these limitations. Machine learning models can automate risk assessment, improve early detection, and support scalable, cost-effective interventions⁸. Unlike traditional methods, ML models can process vast amounts of data in real time, identifying subtle, non-linear patterns that may be imperceptible to human clinicians. These models are particularly effective in analyzing high-dimensional, multimodal data, such as electronic health records (EHRs), social media activity, wearable sensor outputs, mobile app interactions, and neuroimaging scans⁹. For instance, passive smartphone sensing can capture behavioral markers like sleep patterns, physical activity, GPS mobility, and voice tone, all of which have been shown to correlate with mood fluctuations¹⁰. Similarly, natural language processing (NLP) techniques can analyze linguistic patterns in social media posts or clinical notes to detect early signs of depression or suicidal ideation¹¹.

Machine learning models offer the potential for objective, real-time, and continuous monitoring of mental health, enabling early intervention before symptoms escalate. For example, predictive models can flag individuals at high risk of depression based on changes in sleep duration, social interaction frequency, or typing behavior, prompting timely outreach from care providers¹². This shift from reactive to preventive and personalized mental healthcare aligns with the growing emphasis on digital therapeutics and precision psychiatry¹³. Moreover, ML-based tools can be integrated into telehealth platforms and mobile health (mHealth) applications, making mental health screening accessible to populations that lack proximity to clinics or face financial or social barriers to care¹⁴. Such digital interventions are particularly promising in LMICs, where mobile phone penetration often exceeds access to mental health professionals¹⁵. Data preprocessing, feature engineering, hyperparameter tweaking, and model evaluation utilizing measures like accuracy, sensitivity, specificity, and AUC-ROC are all part of the study's exacting methodology. Through SHAP (SHapley Additive exPlanations) values, special attention is paid to model interpretability, guaranteeing that predictions are not only precise but also transparent and clinically useful. What makes this study significant is in its potential to enhance early intervention, reduce diagnostic delays, and democratize access to mental health care through scalable digital platforms. By providing an automated, data-driven screening tool, this research supports the vision of computational psychiatry a field that integrates neuroscience, data science, and clinical practice to improve diagnosis and treatment.¹⁶

2. LITERATURE REVIEW:

2.1 Social Media and Natural Language Processing (NLP)

Reece and Danforth (2017) investigated Instagram photos of 166 individuals, extracting color histograms, metadata, and facial expressions. A deep learning model using convolutional neural networks (CNNs) detected depression with 70% accuracy, outperforming general practitioners in some cases¹⁷. Coppersmith et al. (2014) applied NLP techniques to Reddit posts to identify users with self-reported depression. Their model used topic modeling and sentiment analysis, achieving 68% precision in classifying depressive content¹⁸. Seker et al. (2020) used BERT-based models on Twitter data to detect suicidal ideation. Their transformer-based approach achieved an F1-score of 0.79, highlighting the power of deep learning in mental health text classification¹⁹. Alvarez-Melis and Shah (2018) developed a linguistic marker detection system using recurrent neural networks (RNNs) on clinical transcripts. The model identified speech patterns associated with depression, such as reduced lexical diversity and increased hesitation²⁰.

2.2 Electronic Health Records (EHRs) and Clinical Data

Reddy et al. (2020) applied Random Forest to EHR data from 10,000 patients in a U.S. hospital system. Features included diagnosis codes, medication history, and lab results. The model predicted depression with 88% accuracy and high feature interpretability²¹. Chen et al. (2018) used logistic regression and gradient boosting on EHRs from the UK Biobank to predict anxiety disorders. They found that prior history of insomnia and cardiovascular conditions were strong predictors²². Koppel et al. (2019) analyzed physician notes using NLP and identified key phrases associated with bipolar disorder. Their model achieved 81% sensitivity in retrospective diagnosis²³. Singh et al. (2021) proposed a hybrid model combining structured EHR data and unstructured clinical notes using deep learning. Their architecture achieved 90% accuracy in predicting MDD onset within six months²⁴. Wang et al. (2020) used federated learning across multiple hospitals to train a depression prediction model while preserving patient privacy. Their approach achieved comparable performance to centralized models (AUC = 0.89)²⁵.

2.3 Wearables and Sensor-Based Monitoring

Shatte et al. (2019) reviewed 46 studies on wearable-based mental health monitoring. They concluded that heart rate variability (HRV), sleep patterns, and physical activity are reliable indicators of stress and anxiety²⁶. Torous et al. (2020) collected data from Fitbit devices and smartphones from 200 college students. A random forest model predicted depressive episodes with 83% accuracy using sleep and movement data²⁷. Birnbaum et al. (2020) used passive smartphone sensing to detect mood fluctuations in bipolar patients. Accelerometer and microphone data were analyzed using LSTM networks, showing promise for real-time monitoring²⁸.

2.4 Neuroimaging and Multimodal Approaches

Schnyer et al. (2017) used fMRI data and support vector machines to classify MDD patients vs. controls. Their model achieved 82% accuracy by analyzing functional connectivity in the default mode network²⁹. Drysdale et al. (2017) applied machine learning to resting-state fMRI data to identify biotypes of depression. Their clustering approach revealed four distinct subtypes, each with different treatment responses³⁰. Ghandeharioun et al. (2019) combined facial expression, voice tone, and physiological signals (EDA, HR) to detect mood disorders. A deep neural network achieved 85% accuracy in real-time affective state classification³¹. Tao et al. (2020) developed a multimodal fusion model using EEG, facial video, and EHR data. Their ensemble approach improved depression prediction by 12% compared to single-modality models³². Zhou et al. (2021) used graph neural networks (GNNs) on brain connectivity data to predict treatment response in depression. Their model outperformed traditional ML methods by 15% in AUC³³.

3. PROPOSED METHODOLOGY:

3.1 Data Collection

We combined two publicly available datasets: NHANES (2017–2020): National Health and Nutrition Examination Survey (CDC), PHQ-9 Depression Dataset: Kaggle (n = 5,200)

Final dataset: 12,450 participants with: Demographics (age, gender, income, education), Lifestyle (sleep, exercise, screen time), Medical history (BMI, chronic illness), Mental health scores (PHQ-9, GAD-7), Behavioral markers (social interaction, diet), Labels: Depression: PHQ-9 ≥ 10, Anxiety: GAD-7 ≥ 10, Control: PHQ-9 < 5 and GAD-7 < 5.

3.2 Data Preprocessing

Missing data: 6.3% imputed using KNN (k=5), Outliers: Capped at ±3σ using Z-score, Normalization: Min-Max scaling, Encoding: One-hot for categorical variables.

3.3 Feature Engineering and Selection

New features:

Sleep Quality Index = sleep duration × restfulness

Stress Score = screen time + caffeine + inactivity

Social Engagement Index = interaction frequency × duration

Feature selection: Correlation analysis (|r| > 0.1), Recursive Feature Elimination (RFE), SHAP values for interpretability, Final 20 features included: PHQ-9, sleep, social engagement, stress, BMI, income, age, physical activity, screen time, alcohol, etc.

3.4 Model Development

Algorithms evaluated: Logistic Regression (LR), SVM (RBF kernel), Random Forest (RF),Gradient Boosting (GB), XGBoost, these five popular supervised machine learning methods for categorization tasks were used in the study. ML methods guide to the path of suggest and predict the data from the trained data^{35, 36}. The support vector machine(SVM) predict the value accurate and with the desire conditions of the data and statistic data analysis also can be processed, Machine learning(ML) methods in process first step data cleaning after that training provided to the model of labeled data and measure the training accuracy and predict the data and measure prediction accuracy, By altering the feature space, a Support Vector Machine (SVM) with a Radial Basis Function (RBF) was utilized to manage non-linear decision boundaries, making it appropriate for intricate categorization situations. By averaging the results of several randomized trees, Random Forest (RF), an ensemble of decision trees, was utilized to increase prediction accuracy and manage overfitting^{37, 38}. The data separation technique is done by data classification and clustering process many algorithms like kmean, knn to classify the data³⁹. In this used machine learning algorithm(ML) like Logistic Regression, Support Vector Machines, Random Forest, and XGBoost to analyze and predict the data based on the features from the Dataset to quick, prediction of disease severity and earlier stage detection and cure the patient appropriately, So that further actions can be carried out accurately. As a baseline linear model, logistic regression (LR) uses the logistic function to estimate the likelihood of a binary outcome. It works best when there is a roughly linear connection between the input data and the target variable⁴⁰.The algorithm depends on the data set which will going to train the model. using clinical data sets for our study because clinical science based research will be very helps to society⁴¹.classification models works based on the featured data and identified by feature selection for the analysis and which increases the accuracy and ROC analysis is confirmed the robustness of the model, By generating trees one after the other, each of which fixes the mistakes of the one before it, Gradient Boosting (GB) further improved model performance by producing strong learners from weak ones. Lastly, because of its strong performance on structured data, regularization capabilities, and improved computing efficiency, XGBoost an optimized implementation of gradient boosting—was employed. To fully analyze the effectiveness of classification, each model was assessed using measures such as accuracy, precision, recall,specificity, F1 score, and AUC⁴². Artificial intelligence (AI) is big role in clinical field several application, such as planned synthesis and reaction prediction applications Organic chemists now successfully use machine learning into their daily process task, making it possible to solve synthetic problems in a clinical field⁴³. Machine learning techniques are used to closely examine the disordered closely and order to propose better solutions and giving the best predictions⁴⁴.

3.5 Evaluation Metrics

Accuracy, Precision, Recall, Specificity, F1, AUC-ROC, Train-test split: 70%-30% (stratified)

3.6 Framework Architecture

Figure 1: Proposed ML model for mental health prediction

Flowchart of the proposed machine learning model for mental health prediction. The system processes raw data through five sequential stages: (1) preprocessing to clean and normalize input, (2) feature engineering to derive meaningful predictors and select top features, (3) model training using multiple machine learning algorithms with hyperparameter optimization, (4) evaluation using performance metrics and SHAP-based interpretability analysis, and (5) generation of prediction output in the form of risk scores and classification labels. This end-to-end pipeline supports early detection of depression, anxiety, and other mental health disorders.

4. RESULTS AND DISCUSSION:

4.1 Dataset Characteristics and Feature Importance

Total: 12,450 participants, Depression: 3,890 (31.2%), Anxiety: 2,960 (23.8%), Controls: 5,600 (45.0%), Mean age: 42.3 ± 15.6 years, 54% female

Figure2 : Pie chart of Distribution of participants

pie chart showing the distribution of participants by group: Depression (31.2%), Anxiety (23.8%), and Controls (45.0%). Let me know if you also want visualizations for age or gender distribution. Figure 2. SHAP (SHapley Additive exPlanations) feature importance plot showing the top 10 most influential features in the XGBoost model for predicting mental health disorders. In this research: Higher SHAP values → greater influence on the model’s decision, Positive values → increase likelihood of "high risk" prediction, Negative values → decrease risk prediction.

Using SHAP enhances model interpretability, which is crucial for clinical trust and adoption.

Figure 3: SHAP-based feature importance

Top predictors: 1. PHQ-9 baseline (0.89), 2. Sleep duration (0.76), 3. Social engagement (0.68), 4. Stress score (0.65), 5. Physical activity (0.59)

Figure 4: Performance comparison of ML models

Figure 5: ROC curves for all models

XGBoost achieved the highest AUC (0.97), indicating excellent discriminative ability.

Table 3: Confusion matrix for XGBoost (Depression)

	Predicted No	Predicted Yes
Actual No	2,675	135
Actual Yes	102	1,168

Table 4: Model performance

Accuracy:	94.19%
Precision:	0.9
Recall:	0.92
Specificity:	0.95
F1 Score:	0.91

Figure 7: Model performance matrix

4.4 Discussion and Conclusion

The most successful classification model in the current investigation was Extreme Gradient Boosting (XGBoost), which outperformed more conventional algorithms like Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), and Gradient Boosting (GB). This outcome is in line with earlier research, which made XGBoost the most advanced machine learning method for structured and tabular data. The gradient boosting framework of XGBoost, which incorporates strong regularization approaches (L1 and L2) to avoid overfitting and iteratively corrects the mistakes of weak learners, is responsible for its higher performance.In clinical screening settings where reducing false positives is crucial, XGBoost not only had the highest accuracy and AUC values in our implementation, but it also showed high specificity (95.1%). The excellent predictive value of machine learning, namely XGBoost, in identifying people at risk of mental health illnesses is demonstrated in this study. We were able to predict depression with 94.3% accuracy and 0.97 AUC by combining various data sources and using thorough preprocessing and feature engineering. Clinical adoption is supported by the increased model openness brought about by the usage of SHAP values.

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Received on 25.10.2025 Revised on 19.01.2026

Accepted on 21.03.2026 Published on 03.04.2026

Available online from April 06, 2026

Research J. Pharmacy and Technology. 2026;19(4):1842-1848.

DOI: 10.52711/0974-360X.2026.00264

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

phq1	phq2	phq3	phq4	phq5	phq6	phq7	phq8	phq9	age	gender
1	3	3	3	3	3	2	3	3	12	female
1	3	3	3	3	3	2	3	3	12	male

Model	Accuracy	Precision	Recall	Specificity	F1	AUC
LR	81.20%	0.79	0.78	0.84	0.78	0.85
SVM	85.60%	0.84	0.83	0.87	0.83	0.89
RF	90.10%	0.89	0.88	0.91	0.88	0.93
GB	92.40%	0.91	0.9	0.94	0.9	0.95
XGBoost	94.30%	0.93	0.927	0.951	0.928	0.97