Abstract

Mental health disorders are primarily life style driven disorders, which are mostly unidentifiable by clinical or direct observations, but act as a silent killer for the impacted individuals. Using machine learning (ML), the prediction of mental ailments has taken significant interest in medical informatics community especially when clinical indicators are not there. But, majority studies now focus on usual machine learning methods used to predict mental disorders with few organized health data, this may give wrong signals. To overcome the drawbacks of the conventional ML prediction models, this work presents Deep Learning (DL) trained prediction model for automated feature extraction to realistically predict mental health disorders from the online textual posts of individuals indicating suicidal and depressive contents. The proposed model encompasses three phases named pre-processing, feature extraction and optimal prediction phase. The developed model utilizes a novel Sparse Auto-Encoder based Optimal Bi-LSTM (SAE-O-Bi-LSTM) model, which integrates Bi-LSTM and Adaptive Harris-Hawk Optimizer (AHHO) for extracting the most relevant mental illness indicating features from the textual content in the dataset. The dataset utilized for training consist of 232074 unique posts from the "SuicideWatch" and "Depression" subreddits of the Reddit platform during December 2009 to Jan 2021 downloaded from Kaggle. In-depth comparative analysis of the testing results is conducted using accuracy, precisions, F1 score, specificity, and Recall and ROC curve. The results depict considerable improvement for our developed approach with an accuracy of 98.8% and precision of 98.7% respectively, which supports the efficacy of our proposed model.

Keywords

Mental Health, Emotion, Adolescence, Sparse Autoencoder, Adaptive Harris Hawk Optimizer,

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References

  1. S. Srivastava, M. Pant, A. Nagar, Yuva: An e-health model for dealing with psychological issues of adolescents, Journal of Computational Science, Volume 21,2017,Pages 150-163,ISSN 1877-7503, https://doi.org/10.1016/j.jocs.2017.05.030
  2. M. Srividya, S. Mohanavalli, N. Bhalaji, Behavioral Modeling for Mental Health using Machine Learning Algorithms. Journal of Medical Systems, 42(5), (2018) 1-12. https://doi.org/10.1007/s10916-018-0934-5
  3. S. Graham, C. Depp, E.E. Lee, C. Nebeker, X. Tu, H.C. Kim, D.V. Jeste, Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Current Psychiatry Reports, 21(11), (2019) 116. https://doi.org/10.1007/s11920-019-1094-0
  4. J. Andrew, M. Rudra, J. Eunice, R.V. Belfin, Artificial intelligence in adolescents mental health disorder diagnosis, prognosis, and treatment. Frontiers in Public Health, 11, (2023). https://doi.org/10.3389/fpubh.2023.1110088
  5. M. Garcia-Argibay, Y. Zhang-James, S. Cortese, P. Lichtenstein, H. Larsson, S.V. Faraone, Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach. Molecular Psychiatry, 28, (2023) 1232–1239. https://doi.org/10.1038/s41380-022-01918-8
  6. H. Banati, N. Arora, Enabling inclusive education in structured learning environments through social network analysis. International Journal of Innovation in Education, 2(2/3/4), (2014) 151. https://doi.org/10.1504/IJIIE.2014.067933
  7. M.D. Nemesure, M.V. Heinz, R. Huang, N.C. Jacobson, Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence, Scientific Reports, 11(1), (2021) 1980. https://doi.org/10.1038/s41598-021-81368-4
  8. A.M. Chekroud, J. Bondar, J. Delgadillo, G. Doherty, A. Wasil, M. Fokkema, Z. Cohen, D. Belgrave, R. DeRubeis, R. Iniesta, D. Dwyer, K. Choi, The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry, 20(2), (2021) 154–170. https://doi.org/10.1002/wps.20882
  9. M. Macalli, M. Navarro, M. Orri, M. Tournier, R. Thiébaut, S.M. Cote, C. Tzourio, A machine learning approach for predicting suicidal thoughts and behaviours among college students. Scientific reports, 11(1), (2021) 11363. https://doi.org/10.1038/s41598-021-90728-z
  10. H. Komatsu, E. Watanabe, M. Fukuchi, Psychiatric Neural Networks and Precision Therapeutics by Machine Learning. Biomedicines, 9(4), (2021) 403. https://doi.org/10.3390/biomedicines9040403
  11. Das, S., Srivastava, S., Tripathi, A., & Das, S. (2022). Meta-Analysis of EMF-Induced Pollution by COVID-19 in Virtual Teaching and Learning With an Artificial Intelligence Perspective. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 17(4), 1-20. http://doi.org/10.4018/IJWLTT.285566
  12. C.Su, Z. Xu, J. Pathak, F. Wang, Deep learning in mental health outcome research: a scoping review. Translational Psychiatry, 10(1), (2020) 116. https://doi.org/10.1038/s41398-020-0780-3
  13. D.M. Low, K.H. Bentley, S.S. Ghosh, Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investigative Otolaryngology, 5(1), (2020) 96–116. https://doi.org/10.1002/lio2.354
  14. J. Sui, R. Jiang, J. Bustillo, V. Calhoun, Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises. Biological Psychiatry, 88(11), (2020) 818-828. https://doi.org/10.1016/j.biopsych.2020.02.016
  15. G. Colmenarejo, Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review. Nutrients, 12(8), (2020) 2466. https://doi.org/10.3390/nu12082466
  16. M. Arif, A. Basri, G. Melibari, T. Sindi, N. Alghamdi, N. Altalhi, M. Arif, Classification of Anxiety Disorders using Machine Learning Methods: A Literature Review. Insights of Biomedical Research, 4(1), (2020) 95-110. https://doi.org/10.36959/584/455
  17. B. El-Khodary, M. Samara, The relationship between multiple exposures to violence and war trauma, and mental health and behavioural problems among Palestinian children and adolescents. European Child & Adolescent Psychiatry, 29, (2019) 719–731. https://doi.org/10.1007/s00787-019-01376-8
  18. A. Raballo, M. Poletti, Advances in early identification of children and adolescents at risk for psychiatric illness. Current Opinion in Psychiatry, 33(6), (2020) 611–617. https://doi.org/10.1097/YCO.0000000000000652
  19. Srivastava, S., Pant, M. & Agrawal, N. Psychology of adolescents: a fuzzy logic analysis. Int J Syst Assur Eng Manag 9, 66–81 (2018). https://doi.org/10.1007/s13198-016-0472-9
  20. F.Y. Womer, X. Gong, X. Chen, L. Tang, R. Feng, S. Dong, J. Duan, Y. Chen, R. Zhang, Y. Wang, S. Ren, Y. Wang, J. Kang, Z. Yin, Y. Wei, S. Wei, X. Jiang, K. Xu, B. Cao, Y. Zhang, W. Zhang, Y. Tang, X. Zhang & F. Wang, Identifying and validating subtypes within major psychiatric disorders based on frontal–posterior functional imbalance via deep learning. Molecular Psychiatry, 26, (2020) 2991–3002. https://doi.org/10.1038/s41380-020-00892-3
  21. S. Srivastava, N. Agarwal, M. Pant and A. Abraham, "A secured model for Indian e-health system," 2013 9th International Conference on Information Assurance and Security (IAS), Gammarth, Tunisia, 2013, pp. 96-101, doi: http://doi.org/10.1109/ISIAS.2013.6947740
  22. A.E. Tate, R.C. McCabe, H. Larsson, S. Lundstrom, P. Lichtenstein, R. Kuja-Halkola, Predicting mental health problems in adolescence using machine learning techniques. Plos One, 15(4), (2020) e0230389. https://doi.org/10.1371/journal.pone.0230389
  23. J. Chung, J. Teo, Single classifier vs. ensemble machine learning approaches for mental health prediction. Brain Informatics, 10(1), (2023). https://doi.org/10.1186/s40708-022-00180-6
  24. T. Tran, R. Kavuluru, Predicting mental conditions based on ‘history of present illness’ in psychiatric notes with deep neural networks. Journal of Biomedical Informatics, 75S, (2017) S138–S148. https://doi.org/10.1016/j.jbi.2017.06.010
  25. A. Baba, K. Bunji, Prediction of Mental Health Problem Using Annual Student Health Survey: A Machine Learning Approach (Preprint). JMIR Mental Health, 10, (2022) e42420. https://doi.org/10.2196/42420
  26. K. Dheeraj, T. Ramakrishnudu, Negative emotions detection on online mental-health related patients texts using the deep learning with MHA-BCNN model. Expert Systems with Applications, 182, (2021) 115265. https://doi.org/10.1016/j.eswa.2021.115265
  27. N.S. Mohd Shafiee, S. Mutalib, Prediction of Mental Health Problems among Higher Education Student Using Machine Learning. International Journal of Education and Management Engineering, 10, (6), (2020) 1–9. https://doi.org/10.5815/ijeme.2020.06.01
  28. S. Mutalib, N.S. Mohd Shafiee, S. Abdul-Rahman, Mental Health Prediction Models Using Machine Learning in Higher Education Institution. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(5), (2021) 1782–1792. https://doi.org/10.17762/turcomat.v12i5.2181
  29. A.S. Heinsfeld, A.R. Franco, R.C. Craddock, A. Buchweitz, F. Meneguzzi, Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clinical, 17, (2018) 16-23. https://doi.org/10.1016/j.nicl.2017.08.017
  30. N.A. Baghdadi, A. Malki, H.M. Balaha, Y. AbdulAzeem, M. Badawy, M. Elhosseini, An optimized deep learning approach for suicide detection through Arabic tweets. Peer Journal of Computer Science, 8, (2022) e1070. https://doi.org/10.7717/peerj-cs.1070
  31. X. Wang, H. Li, C. Sun, X. Zhang, T. Wang, C. Dong, D. Guo, Prediction of mental health in medical workers during COVID-19 based on machine learning. Frontiers in public health, 9, (2021) 697850. https://doi.org/10.3389/fpubh.2021.697850
  32. R.L. Allesøe, W.K. Thompson, J. Bybjerg-Grauholm, D.M. Hougaard, M. Nordentoft, T. Werge, M.E Benros, Deep learning for cross-diagnostic prediction of mental disorder diagnosis and prognosis using Danish nationwide register and genetic data. JAMA psychiatry, 80(2) (2023) 146-155. https://doi.org/10.1001/jamapsychiatry.2022.4076
  33. S. Tutun, M.E. Johnson, A. Ahmed, A. Albizri, S. Irgil, I. Yesilkaya, E.N. Ucar, T. Sengun, A. Harfouche, An AI-based decision support system for predicting mental health disorders. Information Systems Frontiers, 25(3), (2023)1261-1276. https://doi.org/10.1007/s10796-022-10282-5
  34. J. Hu, Y. Huang, X. Zhang, B. Liao, G. Hou, Z. Xu, S. Dong, P. Li, Identifying suicide attempts, ideation, and non-ideation in major depressive disorder from structural MRI data using deep learning. Asian journal of psychiatry, 82, (2023) 103511. https://doi.org/10.1016/j.ajp.2023.103511
  35. N.K. Iyortsuun, S.H. Kim, M. Jhon, H.J. Yang, S. Pant, A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis. Healthcare, 11(3), (2023) 285. https://doi.org/10.3390/healthcare11030285
  36. S. Ghosal, A. Jain, Depression and Suicide Risk Detection on Social Media using fastText Embedding and XGBoost Classifier. Procedia Computer Science, 218, (2023) 1631–1639. https://doi.org/10.1016/j.procs.2023.01.141
  37. A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, (2019) 849-872. https://doi.org/10.1016/j.future.2019.02.028
  38. A. Wunnava, M.K. Naik, R. Panda, B. Jena, A. Abraham, An adaptive Harris hawks optimization technique for two dimensional grey gradient based multilevel image thresholding. Applied Soft Computing, 95, (2020) 106526. https://doi.org/10.1016/j.asoc.2020.106526
  39. F.S. Gharehchopogh, S. Ghafouri, M. Namazi, B. Arasteh, Advances in manta ray foraging optimization: A comprehensive survey. Journal of Bionic Engineering, 21(2), (2024) 953-990. https://doi.org/10.1007/s42235-024-00481-y
  40. F.S. Gharehchopogh, T. Ibrikci, An improved African vultures optimization algorithm using different fitness functions for multi-level thresholding image segmentation. Multimedia Tools and Applications, 83(6), (2024) 16929-16975. https://doi.org/10.1007/s11042-023-16300-1
  41. F.S. Gharehchopogh, B. Abdollahzadeh, S. Barshandeh, B. Arasteh, A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT. Internet of Things, 24, (2023) 100952. https://doi.org/10.1016/j.iot.2023.100952
  42. F.S. Gharehchopogh, M.H. Nadimi-Shahraki, S. Barshandeh, B. Abdollahzadeh, H. Zamani, Cqffa: A chaotic quasi-oppositional farmland fertility algorithm for solving engineering optimization problems. Journal of Bionic Engineering, 20(1), (2023) 158-183. https://doi.org/10.1007/s42235-022-00255-4
  43. A.A. Joshi, R.M. Aziz, Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data. International Journal of Imaging Systems and Technology, 34(2), (2024) e23007. https://doi.org/10.1002/ima.23007
  44. S. Afreen, Ajay Kumar Bhurjee, Rabia Musheer Aziz, Gene selection with Game Shapley Harris hawks optimizer for cancer classification. Chemometrics and intelligent laboratory systems, 242, (2023) 104989–104989. https://doi.org/10.1016/j.chemolab.2023.104989
  45. A. Yaqoob, Rabia Musheer Aziz, Navneet Kumar Verma, Applications and Techniques of Machine Learning in Cancer Classification: A Systematic Review. Human-Centric Intelligent Systems, 3, (2023) 588–615. https://doi.org/10.1007/s44230-023-00041-3