By: Chaitra Poojari, Nyamatulla Patel, and Ziaullah Choudhari
1- Student, Department of MCA, Secab Institute of Engineering and Technology Vijayapur, India
2- Associate Professor, Department of MCA, Secab Institute of Engineering and Technology Vijayapur
3- Associate Professor, Department of Electronics and Communication and Engineering, Secab Institute of Engineering and Technology, Vijayapur
Abstract– Identifying the psychological instability in mental health assessment through the application of ML techniques, using principally the RFA (Random Forest Algorithm). This research investigates the application of machine learning techniques to detect psychological instability in individuals. By employing a variety of algorithms, including both supervised and unsupervised learning methods, this study aims to predict psychological states based on diverse data inputs such as behavioral patterns, physiological signals, and social interactions. The models are developed and validated using datasets from clinical studies, social media activity, and wearable health devices. The results illustrate the capability of ML to provide accurate and timely predictions of psychological instability, offering valuable insights for early diagnosis and intervention in mental health care. This study advances the field by demonstrating a data-driven approach to understanding and managing psychological health.
Keywords– RFA, machine learning, psychological health, mental health care, social interactions
Citation:
Refrences:
- Subhani AR, Mumtaz W, Saad MN, Kamel N, Malik AS. Machine learning framework for the detection of mental stress at multiple levels. IEEE Access. 2017 Jul 5;5:13545-56.
- Zogan HA. Deep Learning-Based Frameworks for Automated Identifying Depression Through Social Media(Doctoral dissertation).
- Jadhav R, Chellwani V, Deshmukh S, Sachdev H. Mental disorder detection: Bipolar disorder scrutinization using machine learning. In2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) 2019 Jan 10 (pp. 304-308). IEEE.
- Azar G, Gloster C, El-Bathy N, Yu S, Neela RH, Alothman I. Intelligent data mining and machine learning for mental health diagnosis using genetic algorithm. In2015 IEEE International Conference on Electro/Information Technology (EIT) 2015 May 21 (pp. 201-206). IEEE.
- American Psychiatric Association DS,. Diagnostic and statistical manual of mental disorders: DSM-5. Washington, DC: 2013 May 22.Last retrived 7th july 2019.
- Mohiuddin Ahmed, AbdumNaser Mahmood,and Jinkaum Hu,”A study of netwok anomaly detection strategies,”J.Netw.Comput.Appl.,vol.60,no,C,pp.19-31,2016
- Duffner S, Garcia C. An online backpropagation algorithm with validation error-based adaptive learning rate. International Conference on Artificial Neural Networks 2007 Sep 9 (pp. 249-258). Berlin, Heidelberg: Springer Berlin Heidelberg.
- Chauhan R, Kaur H. A feature-based selection technique for reduction of large-scale data. International Journal of Data Analysis Techniques and Strategies. 2017;9(3):207-21.
- Tate AE, McCabe RC, Larsson H, Lundström S, Lichtenstein P, Kuja-Halkola R. Predicting mental health problems in adolescence using machine learning techniques. PloS one. 2020 Apr 6;15(4):e0230389.
- Macnab CJ. A feedback model of human personalities. In2020 IEEE International Systems Conference (SysCon) 2020 Aug 24 (pp. 1-8). IEEE.
- Saha B, Nguyen T, Phung D, Venkatesh S. A framework for classifying online mental health-related communities with an interest in depression. IEEE journal of biomedical and health informatics. 2016 Mar 18;20(4):1008-15.
- Sau A, Bhakta I. Screening of anxiety and depression among seafarers using machine learning technology. Informatics in Medicine Unlocked. 2019 Jan 1;16:100228.
- Ahmad H, Asghar MU, Asghar MZ, Khan A, Mosavi AH. A hybrid deep learning technique for personality trait classification from text. IEEE Access. 2021 Oct 21;9:146214-32.
- Sumathi MR, Poorna B. Prediction of mental health problems among children using machine learning techniques. International Journal of Advanced Computer Science and Applications. 2016;7(1).
- Shatte AB, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychological medicine. 2019 Jul;49(9):1426-48.
- McCormick U, Murray B, McNew B. Diagnosis and treatment of patients with bipolar disorder: A review for advanced practice nurses. Journal of the American Association of Nurse Practitioners. 2015 Sep 1;27(9):530-42.
- Isometsä E, Suominen K, Mantere O, Valtonen H, Leppämäki S, Pippingsköld M, Arvilommi P. The mood disorder questionnaire improves recognition of bipolar disorder in psychiatric care. BMC psychiatry. 2003 Dec;3:1-4.
- Van Zaane J, van den Berg B, Draisma S, Nolen WA, van den Brink W. Screening for bipolar disorders in patients with alcohol or substance use disorders: performance of the mood disorder questionnaire. Drug and Alcohol Dependence. 2012 Aug 1;124(3):235-41.
- Hirschfeld RM. The Mood Disorder Questionnaire: a simple, patient-rated screening instrument for bipolar disorder. Primary care companion to the Journal of Clinical Psychiatry. 2002;4(1):9.
- Nederlof M, Heerdink ER, Egberts AC, Wilting I, Stoker LJ, Hoekstra R, Kupka RW. Monitoring of patients treated with lithium for bipolar disorder: an international survey. International journal of bipolar disorders. 2018 Dec;6:1-9.
