An Approach to Determine and Categorize Mental Health Condition using Machine Learning and Deep Learning Models


  • B. H. Bhavani JSS Academy of Technical Education, Bengaluru, Karnataka, India
  • N. C. Naveen JSS Academy of Technical Education, Bengaluru, Karnataka, India
Volume: 14 | Issue: 2 | Pages: 13780-13786 | April 2024 |


The mental health of the human population, particularly in India during and after the COVID-19 pandemic is a major concern. All age groups have undergone mental stress during and after COVID-19, especially college students in urban areas and individuals belonging to the age group from 16 to 25. Early detection of mental stress among urban students will help in the resolution of major related issues that may hurt one's career. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have enabled the prediction of mental health status. Numerous studies have been conducted using various approaches, but there is still no agreement on how to predict mental symptoms across age groups. In the current study, proposed DL, Long Short-Term Memory (LSTM), and ML models, namely Support Vector Machine (SVM), ADA Boost, Random Forest (RF), K-Nearest Neighbor (K-NN), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) are trained and tested on a real-world dataset. The DL LSTM model outperformed the conventional ML models with an accuracy of 100%.


mental health, machine learning, health status detection, mental health data


Download data is not yet available.


K. Moghe, D. Kotecha, and M. Patil, "COVID-19 and Mental Health: A Study of its Impact on Students in Maharashtra, India." medRxiv, Feb. 05, 2021.

P. Bhakat and K. Das, "Status of mental health among college and university students during first and second wave of COVID-19 outbreak in India: A cross-sectional study," Journal of Affective Disorders Reports, vol. 12, Apr. 2023, Art. no. 100494.

S. Sengupta, S. Mugde, and G. Sharma, "An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques." medRxiv, Aug. 04, 2020.

S. Deb, S. Kar, S. Deb, S. Biswas, A. A. Dar, and T. Mukherjee, "A Cross-Sectional Study on Mental Health of School Students during the COVID-19 Pandemic in India," Data, vol. 7, no. 7, Jul. 2022, Art. no. 99.

R. Sharma, S. D. Pagadala, P. Bharti, S. Chellappan, T. Schmidt, and R. Goyal, "Assessing COVID-19 Impacts on College Students via Automated Processing of Free-form Text." arXiv, Dec. 16, 2020.

H. Yasmin, S. Khalil, and R. Mazhar, "Covid 19: Stress Management among Students and its Impact on Their Effective Learning," International Technology and Education Journal, vol. 4, no. 2, pp. 65–74, 2020.

S. Ray, "Mental and Psychosocial Health: A Post-COVID Concern in India," Neurology India, vol. 70, no. 5, pp. 2116–2120, 2022.

S. Grover et al., "Psychological impact of COVID-19 lockdown: An online survey from India," Indian Journal of Psychiatry, vol. 62, no. 4, pp. 354–362, 2020.

S. Ray, V. Goswami, and C. M. Kumar, "Stress-The hidden pandemic for school children and adolescents in India during COVID-19 era," Current Psychology (New Brunswick, N.J.), pp. 1–10, Feb. 2022.

Y.-J. Zhao et al., "Post COVID-19 mental health symptoms and quality of life among COVID-19 frontline clinicians: a comparative study using propensity score matching approach," Translational Psychiatry, vol. 12, no. 1, pp. 1–7, Sep. 2022.

T. Zhang, A. M. Schoene, S. Ji, and S. Ananiadou, "Natural language processing applied to mental illness detection: a narrative review," npj Digital Medicine, vol. 5, no. 1, pp. 1–13, Apr. 2022.

A. P. Chaudhary, N. S. Sonar, J. Tr, M. Banerjee, and S. Yadav, "Impact of the COVID-19 Pandemic on the Mental Health of College Students in India: Cross-sectional Web-Based Study," JMIRx med, vol. 2, no. 3, 2021, Art. no. e28158.

K. Chaturvedi, D. K. Vishwakarma, and N. Singh, "COVID-19 and its impact on education, social life and mental health of students: A survey," Children and Youth Services Review, vol. 121, Feb. 2021, Art. no. 105866.

A. Mahapatra and P. Sharma, "Education in times of COVID-19 pandemic: Academic stress and its psychosocial impact on children and adolescents in India," International Journal of Social Psychiatry, vol. 67, no. 4, pp. 397–399, Jun. 2021.

S. Tang, M. Xiang, T. Cheung, and Y.-T. Xiang, "Mental health and its correlates among children and adolescents during COVID-19 school closure: The importance of parent-child discussion," Journal of Affective Disorders, vol. 279, pp. 353–360, Jan. 2021.

S. Patra, B. K. Patro, and S. P. Acharya, "COVID-19 lockdown and school closure: Boon or bane for child mental health, results of a telephonic parent survey," Asian Journal of Psychiatry, vol. 54, Dec. 2020, Art. no. 102395.

S. A. Lawrence, J. Garcia, C. Stewart, and C. Rodriguez, "The mental and behavioral health impact of COVID-19 stay at home orders on social work students," Social Work Education, vol. 41, no. 4, pp. 707–721, May 2022.

S. Sundarasen et al., "Psychological Impact of COVID-19 and Lockdown among University Students in Malaysia: Implications and Policy Recommendations," International Journal of Environmental Research and Public Health, vol. 17, no. 17, Sep. 2020, Art. no. 6206.

A. Kecojevic, C. H. Basch, M. Sullivan, and N. K. Davi, "The impact of the COVID-19 epidemic on mental health of undergraduate students in New Jersey, cross-sectional study," PLoS ONE, vol. 15, no. 9, Sep. 2020, Art. no. e0239696.

N. Gadi, S. Saleh, J.-A. Johnson, and A. Trinidade, "The impact of the COVID-19 pandemic on the lifestyle and behaviours, mental health and education of students studying healthcare-related courses at a British university," BMC Medical Education, vol. 22, no. 1, Feb. 2022, Art. no. 115.

G. Anuradha and D. N. Jamal, "Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network," Engineering, Technology & Applied Science Research, vol. 11, no. 3, pp. 7135–7139, Jun. 2021.

K. Koklonis, M. Sarafidis, M. Vastardi, and D. Koutsouris, "Utilization of Machine Learning in Supporting Occupational Safety and Health Decisions in Hospital Workplace," Engineering, Technology & Applied Science Research, vol. 11, no. 3, pp. 7262–7272, Jun. 2021.

S. A. A. Biabani and N. A. Tayyib, "A Review on the Use of Machine Learning Against the Covid-19 Pandemic," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 8039–8044, Feb. 2022.

G. Anuradha, N. Jamal, and S. Rafiammal, "Detection of dementia in EEG signal using dominant frequency analysis," in 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, Sep. 2017, pp. 710–714.

J. Kim, J. Lee, E. Park, and J. Han, "A deep learning model for detecting mental illness from user content on social media," Scientific Reports, vol. 10, no. 1, Jul. 2020, Art. no. 11846.

A. Vázquez-Romero and A. Gallardo-Antolín, "Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks," Entropy, vol. 22, no. 6, Jun. 2020, Art. no. 688.

G. Van Houdt, C. Mosquera, and G. Nápoles, "A review on the long short-term memory model," Artificial Intelligence Review, vol. 53, no. 8, pp. 5929–5955, Sep. 2020.


How to Cite

B. H. Bhavani and N. C. Naveen, “An Approach to Determine and Categorize Mental Health Condition using Machine Learning and Deep Learning Models”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13780–13786, Apr. 2024.


Abstract Views: 88
PDF Downloads: 113

Metrics Information