Preventing Student’s Mental Health Problems with the Help of Data Mining


  • Md. Nazmul Hossain
  • Nafiz Fahad
  • Rasel Ahmed
  • Anik Sen
  • Md. Sadi Al Huda
  • Md. Ismail Hossen



Mental health, university students, addiction, decision tree, psychology


The increasing incidence of mental health issues among university students has become a significant concern, often referred to as a "mental health crisis" in academic settings. This study addresses the challenge of predicting mental health issues in university students using data mining techniques. The research involved the creation of a new dataset via a survey method focused on university students, covering various factors like behavioral traits, health conditions, and lifestyle choices. Data mining algorithms such as Naive Bayes, Random Forest, SVM, KNN, and Decision Tree were employed to predict mental health status. The study included dataset collection, cleaning, integration, transformation, reduction, discretization, and the use of Weka and Orange for data analysis. Therefore, exploratory analysis revealed that 53.4% of students reported depression, with a higher incidence among male students and those less involved in extracurricular activities. Predictive analysis showed Naive Bayes as the most accurate algorithm (65.91%) for this prediction task, followed by Random Forest, SVM, KNN, and Decision Tree. The performance was evaluated using accuracy, F1-Score, precision, recall, AUC, and CA. The study highlights the correlation between various aspects of university students' lives and depression. Active participation in extracurricular activities was found to lower depression risks. The effectiveness of data mining in understanding and predicting mental health issues in university students was established, with Naive Bayes being the most effective algorithm for this purpose.


E. Beresin, “The college mental health crisis: Focus on overall wellbeing,” Psychology Today. [Online]. Available at:

R. Kadison, College of the Overwhelmed: The Campus Mental Health Crisis and What to do About it, Jossey-Bass, 2004, 304 p.

C. Loughlin, & J. Barling, “Young workers’ work values, attitudes, and behaviours,” Journal of Occupational and Organizational Psychology, vol. 74, issue 4, pp. 543–558, 2001.

J. J. Arnett, “Emerging adulthood: A theory of development from the late teens through the twenties,” American Psychologist, vol. 55, issue 5, pp. 469-480, 2000.

S. E. Ross, B. C. Niebling, T. M. Heckert, “Sources of stress among college students,” College Student Journal, vol. 33, issue 2, pp. 312-312, 1999.

J. M. Sprung, A. Rogers, “Work-life balance as a predictor of college student anxiety and depression,” Journal of American College Health, vol. 69, issue 7, pp. 775-782, 2021.

M. Luo, “Research on students’ mental health based on data mining algorithms,” Journal of Healthcare Engineering, vol. 2021, Article ID 1382559, 2021.

J. Liu, G. Shi, J. Zhou, Q. Yao, “Prediction of college students’ psychological crisis based on data mining,” Mobile Information Systems, vol. 2021, Article ID 9803212, pp. 1-7, 2021.

J. Qinghua, “Data mining and management system design and application for college student mental health,” Proceedings of the 2016 IEEE International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS’2016), December 2016, pp. 410-413.

H. Alharthi, “Predicting the level of generalized anxiety disorder of the coronavirus pandemic among college age students using artificial intelligence technology,” Proceedings of the 2020 19th IEEE International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES’2020), October 2020, pp. 218-221.

V. Laijawala, A. Aachaliya, H. Jatta, & V. Pinjarkar, “Classification algorithms based mental health prediction using data mining,” Proceedings of the 2020 5th IEEE International Conference on Communication and Electronics Systems (ICCES’2020), June 2020, pp. 1174-1178.

E. Saravia, C. H. Chang, R. J. De Lorenzo, & Y. S. Chen, “MIDAS: Mental illness detection and analysis via social media,” Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2016), August 2016, pp. 1418-1421.

A. A. Choudhury, M. R. H. Khan, N. Z. Nahim, S. R. Tulon, S. Islam, & A. Chakrabarty, “Predicting depression in Bangladeshi undergraduates using machine learning,” Proceedings of the 2019 IEEE Region 10 Symposium (TENSYMP’2019), June 2019, pp. 789-794.

A. E. Tate, R. C. McCabe, H. Larsson, S. Lundström, P. Lichtenstein, & R. Kuja-Halkola, “Predicting mental health problems in adolescence using machine learning techniques,” PloS one, vol. 15, issue 4, e0230389, 2020.

U. S. Reddy, A. V. Thota, & A. Dharun, “Machine learning techniques for stress prediction in working employees,” Proceedings of the 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC’2018), December 2018, pp. 1-4.

A. Hasanbasic, M. Spahic, D. Bosnjic, V. Mesic, & O. Jahic, “Recognition of stress levels among students with wearable sensors,” Proceedings of the 2019 18th IEEE International Symposium INFOTEH-JAHORINA (INFOTEH’2019), March 2019, pp. 1-4.

K. Adams, C. Halacas, M. Cincotta, & C. Pesich, “Mental health and Victorian aboriginal people: what can data mining tell us?” Australian Journal of Primary Health, vol. 20, issue 4, pp. 350-355, 2014.

J. Li, “Analysis of the mental health of urban migrant children based on cloud computing and data mining algorithm models,” Scientific Programming, vol. 2021, pp. 1-7, 2021.

R. Vanlalawmpuia, & M. Lalhmingliana, “Prediction of depression in social network sites using data mining,” Proceedings of the 2020 4th IEEE International Conference on Intelligent Computing and Control Systems (ICICCS’2020), May 2020, pp. 489-495.

S. D'monte, & D. Panchal, “Data mining approach for diagnose of anxiety disorder,” Proceedings of the IEEE International Conference on Computing, Communication & Automation, May 2015, pp. 124-127.

J. Diederich, A. Al-Ajmi, & P. Yellowlees, “Ex-ray: Data mining and mental health,” Applied Soft Computing, vol. 7, issue 3, pp. 923-928, 2007.

H. Zhang, L. Jiang, & L. Yu, “Attribute and instance weighted naive Bayes,” Pattern Recognition, vol. 111, 107674, 2021.

H. Chen, S. Hu, R. Hua, & X. Zhao, “Improved naive Bayes classification algorithm for traffic risk management,” EURASIP Journal on Advances in Signal Processing, vol. 2021, issue 1, pp. 1-12, 2021.

I. Blanco-Montenegro, R. De Ritis, & M. Chiappini, “Imaging and modelling the subsurface structure of volcanic calderas with high-resolution aeromagnetic data at Vulcano (Aeolian Islands, Italy),” Bulletin of Volcanology, vol. 69, pp. 643-659, 2007.

V. Jackins, S. Vimal, M. Kaliappan, & M. Y. Lee, “AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes,” The Journal of Supercomputing, vol. 77, pp. 5198-5219, 2021.

M. Ritonga, M. A. A. Ihsan, A. Anjar, & F. H. Rambe, “Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm,” IOP Conference Series: Materials Science and Engineering, vol. 1088, no. 1, 012045, 2021.

N. M. Abdulkareem, A. M. Abdulazeez, D. Q. Zeebaree, & D. A. Hasan, “COVID-19 world vaccination progress using machine learning classification algorithms,” Qubahan Academic Journal, vol. 1, issue 2, pp. 100-105, 2021.

P. Sadorsky, “A random forests approach to predicting clean energy stock prices,” Journal of Risk and Financial Management, vol. 14, issue 2, p. 48, 2021.

R. C. Chen, C. Dewi, S. W. Huang, & R. E. Caraka, “Selecting critical features for data classification based on machine learning methods,” J. Big Data, vol. 7, issue 1, pp. 1–26, 2020.

Z. Xu, D. Yu, & X. Wang, “A bibliometric overview of International Journal of Machine Learning and Cybernetics between 2010 and 2017,” International Journal of Machine Learning and Cybernetics, vol. 10, pp. 2375-2387, 2019.

A. Kurani, P. Doshi, A. Vakharia, & M. Shah, “A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting,” Annals of Data Science, vol. 10, issue 1, pp. 183-208, 2023.

B. S. Bhati, & C. S. Rai, “Intrusion detection technique using Coarse Gaussian SVM,” International Journal of Grid and Utility Computing, vol. 12, issue 1, pp. 27-32, 2021.

D. Zhao, X. Hu, S. Xiong, J. Tian, J. Xiang, J. Zhou, & H. Li, “K-means clustering and kNN classification based on negative databases,” Applied Soft Computing, vol. 110, 107732, 2021.

X. Luo, D. Li, Y. Yang, & S. Zhang, “Spatiotemporal traffic flow prediction with KNN and LSTM,” Journal of Advanced Transportation, vol. 2019, pp. 1-10, 2019.

T. R. Lee, W. T. Wood, & B. J. Phrampus, “A machine learning (kNN) approach to predicting global seafloor total organic carbon,” Global Biogeochemical Cycles, vol. 33, issue 1, pp. 37-46, 2019.

S. Park, S. Y. Hamm, & J. Kim, “Performance evaluation of the GIS-based data-mining techniques decision tree, random forest, and rotation forest for landslide susceptibility modeling,” Sustainability, vol. 11, issue 20, 5659, 2019.

S. Križanić, “Educational data mining using cluster analysis and decision tree technique: A case study,” International Journal of Engineering Business Management, vol. 12, 1847979020908675, 2020.

F. Sahlan, F. Hamidi, M. Z. Misrat, M. H. Adli, S. Wani, & Y. Gulzar, “Prediction of mental health among university students,” International Journal on Perceptive and Cognitive Computing, vol. 7, issue 1, pp. 85-91, 2021.

N. Fahad, K. M. Goh, M. I. Hossen, K. S. Shopnil, I. J. Mitu, M. A. H. Alif, & C. Tee, “Stand up against bad intended news: An approach to detect fake news using machine learning,” Emerging Science Journal, vol. 7, issue 4, pp. 1247-1259, 2023.

N. Fahad, K. O. M. Goh, M. I. Hossen, C. Tee, & M. A. Ali, “Building a fortress against fake news: Harnessing the power of subfields in artificial intelligence,” Journal of Telecommunications and the Digital Economy, vol. 11, issue 3, pp. 68-83, 2023.




How to Cite

Hossain, M. N., Fahad, N., Ahmed, R., Sen, A., Al Huda, M. S., & Hossen, M. I. (2024). Preventing Student’s Mental Health Problems with the Help of Data Mining. International Journal of Computing, 23(1), 101-108.