Mental Health Diagnosis (Chronic Fatigue Syndrome and Depression) using Decision Tree Algorithm
DOI:
https://doi.org/10.70609/g-tech.v9i3.7595Keywords:
Mental Health, Chronic Fatigue Syndrome, Depression, Decision TreeAbstract
Mental health is an important aspect that affects an individual's life, impacting productivity, social relationships and overall quality of life. The World Health Organization (WHO) states that one in four people worldwide will face mental health challenges. With the increasing incidence of conditions such as depression and Chronic Fatigue Syndrome (CFS), effective detection and intervention methods are urgently needed. Data mining, specifically using Decision Tree algorithms, presents a promising approach to address this challenge. This study utilizes a quantitative methodology to classify depression and CFS patients using a public dataset. The data collection from Kaggle included variables such as demographics and clinical evaluations, consisting of 1,000 records and 15 predictive attributes. Data preprocessing addressed noise, specifically missing values, to ensure model accuracy above 80%. A Decision Tree was implemented, displaying the interpretability of the method by partitioning the data based on the selected attributes. Evaluation metrics such as accuracy, precision, recall, and F1 score showed accuracy of 99% and precision and recall of 100%. The results emphasize the potential of the Decision Tree in distinguishing between depression and CFS, enabling early intervention through accurate patient identification. This study advocates the integration of such machine learning models into clinical practice to improve mental health diagnostics and management, by addressing an important aspect of public health.
References
Aliyev, A. (2025, June 8). ME/CFS vs Depression Classification Dataset. https://www.kaggle.com/datasets/storytellerman/mecfs-vs-depression-classification-dataset
Arunakumari, B. N., Patel, T., Patil, S., Roopa, L. S., & Singh, V. (2023). Enhancing the Quality and Efficiency of Mental Health Care using Decision Trees. 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023. https://doi.org/10.1109/ICCCNT56998.2023.10306470
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. In Classification and Regression Trees. https://doi.org/10.1201/9781315139470
Centers for Disease Control and Prevention. (2024, May 10). Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Centers for Disease Control and Prevention. https://www.cdc.gov/me-cfs/about/index.html
Chauhan, N. S. (2020). Model Evaluation metrics in Machine learning. Kdnuggets.
Fukuda, K., Straus, S. E., Hickie, I., Sharpe, M. C., Dobbins, J. G., Komaroff, A., Schluederberg, A., Jones, J. F., Lloyd, A. R., Wessely, S., Gantz, N. M., Holmes, G. P., Buchwald, D., Abbey, S., Rest, J., Levy, J. A., Jolson, H., Peterson, D. L., Vercoulen, J. H. M. M., … Reeves, W. C. (1994). The Chronic Fatigue Syndrome: A Comprehensive Approach to Its Definition and Study. Annals of Internal Medicine, 121(12). https://doi.org/10.7326/0003-4819-121-12-199412150-00009
Hamner, B., & Frasco, M. (2018). Metrics: Evaluation metrics for machine learning. In R package version 0.1.
Joseph, V. R. (2022). Optimal ratio for data splitting. Statistical Analysis and Data Mining, 15(4). https://doi.org/10.1002/sam.11583
Mehedi Shamrat, F. M. J., Chakraborty, S., Billah, M. M., Das, P., Muna, J. N., & Ranjan, R. (2021). A comprehensive study on pre-pruning and post-pruning methods of decision tree classification algorithm. Proceedings of the 5th International Conference on Trends in Electronics and Informatics, ICOEI 2021. https://doi.org/10.1109/ICOEI51242.2021.9452898
Nazari, Z., Nazari, M., Sayed, M., Danish, S., & Kang, D. (2018). Evaluation of Class Noise Impact on Performance of Machine Learning Algorithms. In IJCSNS International Journal of Computer Science and Network Security (Vol. 18, Issue 8).
Nieciecka, A., Tomys-Składowska, J., Lamch, M., Jabłońska, M., Błasik, N., Janiszewska, M., & Wójcik-Kula, A. (2023). Chronic fatigue syndrome – challenge in diagnosis and management: a literature review. Journal of Medical Science. https://doi.org/10.20883/medical.e877
Qinghao, G., Liguo, S., & Sunying, H. (2020). Impact of data set noise on distributed deep learning. Journal of China Universities of Posts and Telecommunications, 27(2). https://doi.org/10.19682/j.cnki.1005-8885.2020.1005
Saseendran, A. T., Setia, L., Chhabria, V., Chakraborty, D., & Roy, A. B. (2019). Impact of Noise in Dataset on Machine Learning Algorithms. Machine Learning Module CS7CS4/CS4404, February.
Setiawan, I., Fina Antika Cahyani, R., & Sadida, I. (2023). Exploring Complex Decision Trees: Unveiling Data Patterns And Optimal Predictive Power. Journal of Innovation And Future Technology (IFTECH), 5(2). https://doi.org/10.47080/iftech.v5i2.2829
Sivananda, Mr. M., & Kumar, Dr. G. K. (2024). Classification and Regression Based on Decision Tree Algorithm for Machine Learning. Interantional Journal Of Scientific Research In Engineering And Management, 08(02). https://doi.org/10.55041/ijsrem28533
Thieme, A., Belgrave, D., & Doherty, G. (2020). Machine Learning in Mental Health: A systematic review of the HCI literature to support the development of effective and implementable ML Systems. In ACM Transactions on Computer-Human Interaction (Vol. 27, Issue 5). https://doi.org/10.1145/3398069
Tiwari, V., Garg, B., & Sharma, U. P. (2020). Significant Impact of Improved Machine Learning Algorithm in The Processes of Large Data Sets. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/cseit206133
Wernigg, R., & Wernigg, M. (2022). A case study for assessing the utility of a decision tree based learning algorithm in mental health inpatient care quality management. European Psychiatry, 65(S1). https://doi.org/10.1192/j.eurpsy.2022.454
WHO. (2022, June 12). Mental health. https://www.who.int/news-room/fact-sheets/detail/mental-health-strengthening-our-response
WHO. (2023, March 31). Depressive disorder (depression). https://www.who.int/news-room/fact-sheets/detail/depression
Xie, H., & Shang, F. (2014). The study of methods for post-pruning decision trees based on comprehensive evaluation standard. 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2014. https://doi.org/10.1109/FSKD.2014.6980959
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Copyright (c) 2025 Ach. Zubairi, Ahmad Homaidi, Irma Yunita, Jarot Dwi Prasetyo, Hermanto Hermanto
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