Veri Odaklı Yaklaşımlar ile Zihinsel İyi Oluş Durumunun Tespiti

Abstract

Ruhsal iyi oluş düzeyi bozuklukları, modern yaşam biçimlerinin en önemli sorunları arasında yer almaktadır ve hastalıkların erken teşhisinin etkili bir önleme için ne kadar önemli olduğu artık bilinen bir gerçektir. Diğer yandan, Makine Öğrenimi (MÖ) algoritmaları günümüzde hastalık tespitinde değerli bir rol oynamaktadır. Bu çalışmanın amacı yalnızca çeşitli ML sınıflandırıcılarının performansını araştırmak değil aynı zamanda ruh sağlığı tanısı için modern bir teknik sunmaktır. Bu bağlamda, araştırmamızda literatürde yaygın olarak bilinen ML sınıflandırıcılarından Bootstrap Aggregating (Bagging), Extremely Randomized Trees (ExtraTrees), Passive-Aggressive, Light Gradient Boosting Machine (LGBM), Perceptron ve Stochastic Gradient Descent (SGD) algoritmaları ele alınmıştır. Seçilen bireylerde ruhsal sağlık hastalıklarına katkıda bulunan faktörleri ele almak için üç aşamalı bir veri işleme yaklaşımı kullanıyoruz: parçalara ayırma, özellik çıkarımı ve sınıflandırma. Ele alınan veri setindeki özellik önemini analiz eden araştırmamız, yaşın, aile geçmişinin ve işyeri ortamının bir çalışanın ruh sağlığı durumu üzerinde önemli bir etkiye sahip olduğunu ortaya koymaktadır.

Keywords

• Makine Öğrenme
• Sınıflandırıcı
• Sağlık

Detection of Mental Well-Being Status Through Data-Driven Approaches

Abstract

Mental well-being disorders are among the most significant challenges in the modern lifestyles, and it is well-established that early detection of diseases is essential for effective prevention. On the other hand, Machine Learning (ML) algorithms currently play a valuable role in disease detection. The aim of this study is not only to investigate the performance of various ML classifiers but also to propose a modern technique for mental health diagnosis. In this context, our research considers Bootstrap Aggregating (Bagging), Extremely Randomized Trees (ExtraTrees), Passive-Aggressive, Light Gradient Boosting Machine (LGBM), Perceptron, and Stochastic Gradient Descent (SGD) algorithms, which are among the widely recognized ML classifiers in literature. To address the factors contributing to mental health illnesses among the selected individuals, we employ a three-phase data processing approach: segmentation, feature extraction, and classification. Analyzing feature importance from the selected dataset, our study highlights the significant impact of age, family history, and workplace environment on a worker's mental health status.

Keywords

• Machine Learning
• Classifier
• Health

References

1. Doupe, P., Faghmous, J. and Basu, S. (2006). Data mining: Introductory and advanced topics, Pearson Education India.
2. Ghahramani, Z. (2004). Unsupervised learning. Advanced lectures on machine learning, Springer; pp. 72-112.
3. Dunham, M.H. (2006). Data mining: Introductory and advanced topics. Pearson Education India.
4. Kotsiantis, S.B. (2007). Supervised Machine Learning: A Review of Classification Techniques. Informatica, 31, 249–268
5. Cortes, C. and Vapnik, V.N. (1995). Support Vector Network, Machine learning, 20, 1-25.
6. Panch, T., Szolovits, P. and Atun, R. (2018). Artificial intelligence, machine learning and health systems. J. Glob. Health, 8(2), 020303.
7. Doupe, P., Faghmous, J. and Basu, S. (2019). Machine Learning for Health Services Researchers. Value in Health, 22, 808-815.
8. Husnain, A., et al. (2024). Advancements in Health through Artificial Intelligence and Machine Learning: A Focus on Brain Health. Revista Española de Documentación Científica, 18(01), 100-123.

9. Gogas, P. and Papadimitriou, T. (2021). Machine Learning in Economics and Finance. Computational Economics, 57, 1-4.
10. Shami, L. and Lazebnik, T. (2024). Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy. Computational Economics, 63, 1459–1476.
11. Ntampaka, M., et al. (2019). A Deep Learning Approach to Galaxy Cluster X-Ray Masses. ApJ, 876, 82.
12. Kangal, E.E., Salti, M. and Aydogdu, O. (2019). Machine learning algorithm in a caloric view point of cosmology. Physics of the Dark Universe, 26, 100369.
13. Tilaver, H., Salti, M., Aydogdu, O. and Kangal, E.E. (2021). Deep learning approach to Hubble parameter. Computer Physics Communications, 261, 107809.
14. Salti, M., Kangal, E.E. and Aydogdu, O. (2021). Evolution of CMB temperature in a Chaplygin gas model from deep learning perspective. Astronomy and Computing, 37, 100504.
15. Chase, R.J., et al. (2022). A Machine Learning Tutorial for Operational Meteorology - Part I: Traditional Machine Learning. Weather and Forecasting, 37(8), 1509–1529.
16. Buster, G., Benton, B.N., Glaws, A. and King, R.N. (2024 High-resolution meteorology with climate change impacts from global climate model data using generative machine learning. Nature Energy, 9, 894-906.
17. Sumathi, M.R. and Poorna, B. (2016). Prediction of Mental Health Problems Among Children Using Machine Learning Techniques. International Journal of Advanced Computer Science and Applications, 7(1), 552-557.
18. Powers, S.I., Hauser, S. T. and Kilner, L.A. (1989). Adolescent mental health. American Psychologist, 44(2), 200–208.
19. Bhugra D., Till A. and Sartorius N. (2013). What is mental health? International Journal of Social Psychiatry, 59(1), 3-4.
20. Reddy, U.S., Thota, A.V. and Dharun, A. (2018). Machine Learning Techniques for Stress Prediction in Working Employees, 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), (pp. 1-4), Madurai, India.
21. Katarya, R. and Maan, S. (2020). Predicting Mental health disorders using Machine Learning for employees in technical and non-technical companies, 2020 IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE), (pp. 1-5), Coimbatore, India.
22. Jain, T., Jain, A., Hada, P.S., Kumar, H., Verma, V. K. and Patni, A. (2021). Machine Learning Techniques for Prediction of Mental Health, 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), (pp1 1606-1613), Coimbatore, India.
23. Garriga, R., Mas, J., Abraha, S. et al. (2022). Machine learning model to predict mental health crises from electronic health records. Nat Med 28, 1240–1248.
24. Rezapour, M. and Hansen, L. (2022). A machine learning analysis of COVID-19 mental health data. Scientific Reports, 12, 14965.
25. Mulye, A. (2021). Mental Health at Workplace. Kaggle.
26. Hall, M.A. (1999). Correlation-based Feature Selection for Machine Learning. PhD Thesis / University of Waikato, Hamilton, New Zealand.
27. Cunningham, S.J., Littin, J. and Witten, I.H. (1997). Applications of machine learning in information retrieval. University of Waikato Technical Report 97/6.
28. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
29. Geurts, P., Ernst, D. and Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3-42.
30. Crammer, K., Dredze, M., & Pereira, F. (2006). Passive-Aggressive Algorithms for Huge Online Tasks. Journal of Machine Learning Research, 7, 551-585.
31. Ke, G. et al. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA.
32. Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review, 65(6), 386-408.
33. Bottou, L. (1998). Stochastic Gradient Descent. Online Learning and Neural Networks, MIT Press.
34. Caruana, R., Niculescu-Mizil, A. (2004). Data mining in metric space: an empirical analysis of supervised learning performance criteria. Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining, Seattle-WA, USA.
35. Labatut, V. and Cherifi, H. (2011). Evaluation of Performance Measures for Classifiers Comparison. Ubiquitous Computing and Communication Journal, 6:21-34.
36. Chen, T., et al. (2019). Prediction of Extubation Failure for Intensive Care Unit Patients Using Light Gradient Boosting Machine. IEEE Access, 7, 150960-150968.
37. Project Jupyter. Jupyter Notebook: The Classic Notebook Interface. Available at the website https://jupyter.org.