A Study and Prediction of Psychological Disorders through Machine Learning

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Year : 2024 | Volume :02 | Issue : 02 | Page : –
By
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Sandeep Mishra,

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Garima Sharma,

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Chirag Rawat,

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Khushi Mishra,

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Krishna Aggarwal,

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Kaustubh Kumar Shukla,

  1. Assistant Professor, Department of Computer Science and Engieering, Dronacharya Group of Institutions, Greater Noida, Uttar Pradesh, India
  2. Student, Department of Computer Science and Engieering, Dronacharya Group of Institutions, Greater Noida, Uttar Pradesh, India
  3. Student, Department of Computer Science and Engieering, Dronacharya Group of Institutions, Greater Noida, Uttar Pradesh, India
  4. Student, Department of Computer Science and Engieering, Dronacharya Group of Institutions, Greater Noida, Uttar Pradesh, India
  5. Student, Department of Computer Science and Engieering, Dronacharya Group of Institutions, Greater Noida, Uttar Pradesh, India
  6. Associate Professor, Department of Electronics & Communication Engineering, Dronacharya Group of Institutions, Greater Noida, Uttar Pradesh, India

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Physical illness is very much visible but not psychological illness therefore, it requires more attention and care. Psychological disorders also known as psychiatric disorders refer to a wide range of conditions affecting a person’s thought process, leading to significant changes in the behavior of an individual. The most prevalent psychological disorders include depression, anxiety disorders, and post-traumatic stress disorder (PTSD). Symptoms of psychological disorders vary greatly but include common symptoms like changes in mood and cognition. These disorders undermine an individual ability to function effectively in daily life, impacting their well-being. An individual’s behavior and way of thinking are impacted by psychological disorders such as post-traumatic stress disorder, anxiety, and depression, which affect their general well-being. They need the right medication, counseling, and assistance. Algorithms for machine learning are essential for both diagnosing and treating these conditions. Through the analysis of vast datasets, the study looks into two widely used supervised learning models, Random Forest, and Decision Trees, which can accurately detect and comprehend the origin and course of various conditions. By using these models in healthcare, professionals will be better able to identify these conditions and understand their underlying causes.

Keywords: psychiatric disorders, random forest, decision trees, supervised, anxiety, depression, stress, treatment, prediction, algorithms

[This article belongs to International Journal of Algorithms Design and Analysis Review (ijadar)]

How to cite this article:
Sandeep Mishra, Garima Sharma, Chirag Rawat, Khushi Mishra, Krishna Aggarwal, Kaustubh Kumar Shukla. A Study and Prediction of Psychological Disorders through Machine Learning. International Journal of Algorithms Design and Analysis Review. 2024; 02(02):-.
How to cite this URL:
Sandeep Mishra, Garima Sharma, Chirag Rawat, Khushi Mishra, Krishna Aggarwal, Kaustubh Kumar Shukla. A Study and Prediction of Psychological Disorders through Machine Learning. International Journal of Algorithms Design and Analysis Review. 2024; 02(02):-. Available from: https://journals.stmjournals.com/ijadar/article=2024/view=0

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References
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  1. Vaishnavi K, Kamath UN, Rao BA, Reddy NS. Predicting mental health illness using machine learning algorithms. InJournal of Physics: Conference Series 2022 (Vol. 2161, No. 1, p. 012021). IOP Publishing.
  2. Pintelas EG, Kotsilieris T, Livieris IE, Pintelas P. A review of machine learning prediction methods for anxiety disorders. InProceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion 2018 Jun 20 (pp. 8-15).
  3. Priya A, Garg S, Tigga NP. Predicting anxiety, depression and stress in modern life using machine learning algorithms. Procedia Computer Science. 2020 Jan 1;167:1258-67.
  4. van der Meer D, Hoekstra PJ, Van Donkelaar M, Bralten J, Oosterlaan J, Heslenfeld D, Faraone SV, Franke B, Buitelaar JK, Hartman CA. Predicting attention-deficit/hyperactivity disorder severity from psychosocial stress and stress-response genes: a random forest regression approach. Translational Psychiatry. 2017 Jun;7(6):e1145-.
  5. Cacheda, F., Fernandez, D., Novoa, F. J., & Carneiro, V. (2019). Early detection of depression: social network analysis and random forest techniques. Journal of medical Internet research, 21(6), e12554: 1-9.
  6. Acharya N, Kar P, Ally M, Soar J. Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach. Applied Sciences. 2024 Feb 18;14(4):1630.
  7. Leightley D, Williamson V, Darby J, Fear NT. Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort. Journal of Mental Health. 2019 Jan 2;28(1):34-41.
  8. Ramos-Lima LF, Waikamp V, Antonelli-Salgado T, Passos IC, Freitas LH. The use of machine learning techniques in trauma-related disorders: a systematic review. Journal of psychiatric research. 2020 Feb 1;121:159-72.
  9. Cohen TR, Fronk GE, Kiehl KA, Curtin JJ, Koenigs M. Clarifying the relationship between mental illness and recidivism using machine learning: A retrospective study. Plos one. 2024 Feb 23;19(2):e0297448.
  10. Luo J, Chen Y, Tao Y, Xu Y, Yu K, Liu R, Jiang Y, Cai C, Mao Y, Li J, Yang Z. Major depressive disorder prediction based on sleep-wake disorders symptoms in US adolescents: a machine learning approach from national sleep research resource. Psychology research and behavior management. 2024 Dec 31:691-703.
  11. Yuan H, Fan XS, Jin Y, He JX, Gui Y, Song LY, Song Y, Sun Q, Chen W. Development of heart failure risk prediction models based on a multi-marker approach using random forest algorithms. Chinese medical journal. 2019 Apr 5;132(07):819-26.
  12. Diksha Goutam, Vanita Rani, Hardeep Singh Saini. Mental Health Illness Prediction With Hybrid Machine Learning Approach. International Research Journal  of  Modernization in Engineering  Technology and  2024;06(01):3893-3898.
  13. Carozza L. Post-Traumatic Stress Disorder and Cognitive Communication Effects. InLanguage Research in Post-Traumatic Stress (pp. 227-254). Routledge.
  14. Jayasri Devi, Adapa Gopi. An Efficient Novel Approach for Early Detection of Mental Health Disorders Through Distributed Machine Learning Paradigms from Public Societal Communication. International Journal of Intelligent Systems and Applications in Engineering, 2023;12(2):767–778.
  15. Gurjar S, Patil C, Suryawanshi R, Adadande M, Khore A, Tarapore N. Mental Health Prediction Using Machine Learning. International Research Journal of Engineering and Technology (IRJET) e-ISSN. 2022:2395-0056.
  16. Hussein AS, Omar WM, Li X, Ati M. Efficient chronic disease diagnosis prediction and recommendation system. In2012 IEEE-EMBS conference on biomedical engineering and sciences 2012 Dec 17 (pp. 209-214). IEEE.
  17. Lee Y, Kim H, Lee Y, Jeong H. Comparison of the prediction model of adolescents’ suicide attempt using logistic regression and decision tree: secondary data analysis of the 2019 Youth Health Risk Behavior Web-Based Survey. Journal of Korean Academy of Nursing. 2021;51(1):40-53.
  18. Airlangga G. Evaluating Machine Learning Models for Mental Health Diagnostics: A Comparative Analysis and Visual Insights. KLIK: Kajian Ilmiah Informatika dan Komputer. 2024 Feb 26;4(4):2058-68.
  19. Shi X, Jiang D, Huang Y, Wang X, Chen Q, Yan J, Tang B. Family history information extraction via deep joint learning. BMC medical informatics and decision making. 2019 Dec;19:1-6.
  20. Katiyar K, Fatma H, Singh S. Predicting Anxiety, Depression and Stress in Women Using Machine Learning Algorithms. InCombating Women’s Health Issues with Machine Learning 2024 (pp. 22-40). CRC Press.
  21. Liu B, Zhang Y, Fang H, Liu J, Liu T, Li L. Efficacy and safety of long-term antidepressant treatment for bipolar disorders–A meta-analysis of randomized controlled trials. Journal of affective disorders. 2017 Dec 1;223:41-8.
  22. Song Y, Tian Y, Fan C, Zheng Q, Huang L, Zhou Z. Employing decision trees to predict cyberbullying victimization among Chinese adolescents and identify subgroups and their shared characteristics. Current Psychology. 2024 Feb 27:1-4.
  23. Sau A, Phadikar S, Bhakta I. Prediction of spirometry parameters of adult Indian population using machine learning technology. Multimedia Tools and Applications. 2024 Feb 24:1-35.
  24. Peng B, Wang R, Zuo W, Liu H, Deng C, Jing X, Hu H, Zhao W, Qin P, Dai L, Chen Z. Distinct correlation network of clinical characteristics in suicide attempters having adolescent major depressive disorder with non-suicidal self-injury. Translational psychiatry. 2024 Mar 5;14(1):134.
  25. Jafari M, Sadeghi D, Shoeibi A, Alinejad-Rokny H, Beheshti A, García DL, Chen Z, Acharya UR, Gorriz JM. Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023. Applied Intelligence. 2024 Jan;54(1):35-79.
  26. Menzies RE, Richmond B, Sharpe L, Skeggs A, Liu J, Coutts‐Bain D. The ‘revolving door’of mental illness: A meta‐analysis and systematic review of current versus lifetime rates of psychological disorders. British Journal of Clinical Psychology. 2024 Jun;63(2):178-96.
  27. Chaudhary A, Agarwal A, Ansari A, Arya D. Psychological Disorder Analysis Using Machine Learning. Journal of Pharmaceutical Negative Results. 2022 Dec 31:3326-33.
  28. Harris MN. Violent Victimization of Youth With Mental Disorders: Does Lifestyles/Routine Activities or Control Perspectives Mediate the Relationship Between Mental Illness and Victimization?. Crime & Delinquency. 2024 Feb 19:00111287241231747.
  29. Gentili E, Franchini G, Zese R, Alberti M, Ferrara M, Domenicano I, Grassi L. Machine learning from real data: A mental health registry case study. Computer Methods and Programs in Biomedicine Update. 2024 Jan 1;5:100132.
  30. Vichare S, Pirjade ST, Parte S, Salunkhe D, Jaunjale S. Mental Health Prediction and Support Application. Grenze International Journal of Engineering & Technology (GIJET). 2024 Jan 22;10.
  31. Kasaudhan H, Shukla KK, Kushwaha R, Sharma K, Gupta U, Sharma A. Early Detection and Analysis of Lung Cancer Using Artificial Intelligence. In2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) 2024 Feb 9 (Vol. 5, pp. 1470-1474). IEEE.
  32. Pandey S, Arora SV, Shukla KK, Priyanka P, Nivetha A, Chaudhary A. Optimizing 3D Printing to Increase the Gain and Bandwidth of Microstrip Antennas: A Research on Implementation and Design. In2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) 2024 Jul 18 (pp. 1-5). IEEE.
  33. Shukla KK, Muthumanickam T. Micro Cantilever Arrays Optimization and Analysis for Healthcare Applications. In2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) 2022 May 9 (pp. 1681-1689). IEEE.

 

Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 19/10/2024
Accepted 22/10/2024
Published 25/10/2024
Total Visits: 44

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