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Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection

  • B. M. Parashiva Murthy , Ik Hesti Agustin , Sowjanya Bharathi , Rayappan Lotus , S. Bathrinath and Rishabh Garg
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Machine Learning in Healthcare
This chapter is in the book Machine Learning in Healthcare

Abstract

Psychological diseases such as depression and anxiety are prevalent globally, and thus there is a need for advanced detection techniques to ensure maximum clinical interventions and patient care. Traditional machine learning techniques, such as random forest, support vector machines, and logistic regression, although efficient, are at the cost of interpretability, computational performance, and predictability in handling advanced mental health data. In this research work, triple neuro-probabilistic ensemble (TNPE) is proposed to be a breakthrough ensemble model appropriate for depression and anxiety detection. TNPE leverages three foundation learners with complementing strengths: extreme learning machine for speed and generalization overall on small-to-medium-sized datasets; probabilistic neural network for probabilistic decision-making in a clinical setting; and CatBoost, specifically crafted for dealing with categorical and mixed-type data. The models are blended via an optimal soft voting ensemble mechanism driven by adaptive weight optimization with particle swarm optimization. The model was trained on a multisource clinical record, mental health surveys, and social media text dataset preprocessed and balanced with methods such as ADASYN. Feature selection was carried out using the Boruta algorithm. TNPE was tested with 10-fold cross-validation and performed better than standard classifiers like k-nearest neighbors, decision tree, Naïve Bayes, multilayer perceptron, gradient boosting machine, and AdaBoost. TNPE performed better on critical metrics: ROC-AUC (0.963), F1-score (0.927), and accuracy (92.6%). Interpretability enhanced by LIME and SHAP, TNPE became a clinical acceptable decision-making system for diagnosing mental health.

Abstract

Psychological diseases such as depression and anxiety are prevalent globally, and thus there is a need for advanced detection techniques to ensure maximum clinical interventions and patient care. Traditional machine learning techniques, such as random forest, support vector machines, and logistic regression, although efficient, are at the cost of interpretability, computational performance, and predictability in handling advanced mental health data. In this research work, triple neuro-probabilistic ensemble (TNPE) is proposed to be a breakthrough ensemble model appropriate for depression and anxiety detection. TNPE leverages three foundation learners with complementing strengths: extreme learning machine for speed and generalization overall on small-to-medium-sized datasets; probabilistic neural network for probabilistic decision-making in a clinical setting; and CatBoost, specifically crafted for dealing with categorical and mixed-type data. The models are blended via an optimal soft voting ensemble mechanism driven by adaptive weight optimization with particle swarm optimization. The model was trained on a multisource clinical record, mental health surveys, and social media text dataset preprocessed and balanced with methods such as ADASYN. Feature selection was carried out using the Boruta algorithm. TNPE was tested with 10-fold cross-validation and performed better than standard classifiers like k-nearest neighbors, decision tree, Naïve Bayes, multilayer perceptron, gradient boosting machine, and AdaBoost. TNPE performed better on critical metrics: ROC-AUC (0.963), F1-score (0.927), and accuracy (92.6%). Interpretability enhanced by LIME and SHAP, TNPE became a clinical acceptable decision-making system for diagnosing mental health.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
https://doi.org/10.1515/9783111541235-006

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
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