TEJAS Journal of Technologies and Humanitarian Science

ISSN : 2583-5599

Open Access | Quarterly | Peer Reviewed Journal

July, 2024 | Volume 03 | Issue 03


Mental Health Prediction Using Machine Learning Algorithms


Vishakha Pathak
Student Scholar, Computer Science, National P.G. College, Lucknow

Author

Kanishka Dwivedi
Student Scholar, Computer Science, National P.G. College, Lucknow

Author

Mahesh Kumar Tiwari
Assistance Professor, Computer Science Department, National PG College, Lucknow, India

Author


📌 DOI: https://doi.org/10.63920/tjths.33003

🔑 Keywords: Mental health; Mental health prediction; Logistic Regression; Random Forest Classifier; Decision Tree Classifier

📅 Publication Date: 20 July, 2024

📜 License:

  • Share — Copy and Redistribute the material
  • Adapt — Remix, Transform, and build upon the material
  • The licensor cannot revoke these freedoms as long as you follow the license terms.

Abstract:

Different mental health issues, such as depression, stress, anxiety, and others, affect life in such a way that it is difficult for personal and professional life to not get severely ruined if not treated on time. The immediate recognition of the condition will ensure proper and best treatment for the patient so that they get the benefit of good health. This research applies Random Forest Classifier, Logistic Regression and Decision Tree Algorithms to evaluate their precision in mental health problem prediction and the stoppage of stress becomes the focus. The evaluation and comparison of their performance are, therefore, useful in bringing out the most reliable way to ensure early intervention for enhanced mental health and support for improved outcomes.

Download Full PDF Paper


References

[1]. Multimodal Machine Learning for Mental Disorder Detection: A Scoping Review by Thuy Trinh Nguyen et al.
[2]. Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning by I. Ameer et al
[3]. Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms by Anu Priya et al.
[4]. Mental health impact of COVID19 and machine learning applications in combating mental disorders: a review by Chirantan Ganguly et al
[5]. Machine Learning for Depression Diagnosis using Twitter data by Krishna Shrestha et al.
[6]. Application of Machine Learning Techniques to Predict Depression in social media by M. R. T. et al.
[7]. Using Machine Learning to Detect and Predict Anxiety and Depression from Digital Health Data by Trevor Cohen et al
[8]. Stress Detection using Machine Learning Algorithms by M. R. et al.
[9]. Predicting Mental Health Problems in College Students with Machine Learning Models by J. Wang et al.
[10]. Machine Learning Approaches to Predict Depression, Anxiety, and Stress from Social Media Data by K. Guntuku et al.
[11]. Predicting Anxiety and Depression Disorders in a Large Sample of Psychiatric Patients Using Machine Learning Models by J. Wu, M. Yan, et al.
[12]. Mental Health Care for All: A Call for Action to Address Disparities in Access, Quality, and Outcomes by P. H. Wise et al.
[13]. Understanding Depression with Machine Learning based on Twitter data by Krishna Shrestha et al
[14]. Stress Detection Using Machine Learning Algorithms by V. R. Archana1, B. M. Devaraju.
[15]. Machine Learning in ADHD and Depression Mental Health Diagnosis: A Survey by C. Nash et al.
[16]. HCET: Hierarchical clinical embedding with topic modelling on electronic health records for predicting future depression by Y. Meng, W. Speier, M. Ong, and C. W. Arnold.
[17]. Graham, S.; Depp, C.; Lee, E.E.; Nebeker, C.; Tu, X.; Kim, H.-C.; Jeste, D.V. Artificial Intelligence for Mental Health and Mental Illnesses: An Overview
[18]. Towards Assessing Changes in Degree of Depression through Facebook by H. A. Schwartz et al.
[19]. Lee EE, Torus J, De Choudhury M, Depp CA, Graham SA, Kim HC, et al. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom.
[20]. Sushmita Goswami, Deepak Kumar Chaubey, The Impact of social media on the Spread of Fake News and the Role of Machine Learning in Detection, TEJAS Journal of Technologies and Humanitarian Science, ISSN-2583-5599, Vol.02, I.01(2023)
[21]. Neha Singha and Yogendra Pratap Singh, Consumer Sentiment Analysis Using Deep Learning, TEJAS Journal of Technologies and Humanitarian Science, ISSN-2583-5599,Vol.02, I.02(2023)