Advanced Multi-Label Classification for Predicting Diverse Diseases from Comprehensive Symptom Data Using Random Forests

Abstract

This paper presents the design and evaluation of an advanced multi-label classification system that predicts multiple diseases from symptom-based input data. Utilizing a dataset comprising 92 symptoms and 282 potential diseases from 653 patient records provided by home healthcare clinics, we applied a Random Forest Classifier within a multi-output framework. To ensure robustness, we compared the Random Forest model with other machine learning algorithms, including Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Gradient Boosting. The Random Forest model achieved the highest accuracy score of 98%, while other models also demonstrated competitive performance. The results indicate that the proposed model can serve as a reliable support tool in clinical environments, assisting in early diagnosis and enhancing the overall quality of care. Our approach distinguishes itself by effectively tackling the inherent multi-label complexity of medical diagnosis with exceptional accuracy.

Keywords

• Multi-label classification
• Random forest
• Healthcare diagnostics
• Machine learning
• Symptom-based disease prediction

References

1. Aslan, Ö., & Yılmaz, A. A. (2021). A new malware classification framework based on deep learning algorithms. IEEE Access, 9, 87936–87951. https://doi.org/10.1109/ACCESS.2021.3089586
2. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
3. Caruana, R., Niculescu-Mizil, A., Crew, G., & Ksikes, A. (2004). Ensemble selection from libraries of models. Proceedings of the 21st International Conference on Machine Learning, 161–168. https://doi.org/10.1145/1015330.1015432
4. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
5. Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., & Gandomi, A. H. (2020). A novel transfer learning-based approach for pneumonia detection in chest X-ray images. Applied Sciences, 10(2), 559. https://doi.org/10.3390/app10020559
6. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
7. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
8. Joly, A., Geurts, P., & Wehenkel, L. (2014). Random forests with random projections of the output space for high dimensional multi-label classification. In T. Calders, F. Esposito, E. Hüllermeier, & R. Meo (Eds.), Machine Learning and Knowledge Discovery in Databases (Vol. 8724, pp. 607–622). Springer. https://doi.org/10.1007/978-3-662-44848-9_39

9. Kulkarni, N. S., & Kotecha, K. (2024). An overview on the advancements of support vector machine models in healthcare applications. Information, 15(4), 235. https://doi.org/10.3390/info15040235
10. Mandrekar, J. N. (2010). Receiver operating characteristic curve in diagnostic test assessment. Journal of Thoracic Oncology, 5(9), 1315–1316. https://doi.org/10.1097/JTO.0b013e3181ec173d
11. Sevinç, Ö. (2025). Disease diagnosis dataset and codes (Computer software). GitHub. https://github.com/somer81/diagnoseDiseases
12. Sevinç, Ö., Yılmaz, A. A., Mehrubeoglu, M., & Cammarata, K. (2025). Real-time clustering of seagrass age categories using deep learning and unsupervised machine learning. International Journal of Computational and Experimental Science and Engineering, 11(2), 2748–2756. https://doi.org/10.22399/ijcesen.2219
13. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1604. https://doi.org/10.1109/JBHI.2017.2767063
14. Tsoumakas, G., & Katakis, I. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 1–13. https://doi.org/10.4018/jdwm.2007070101
15. Wu, Y., Li, L., Xin, B., Hu, Q., Dong, X., & Li, Z. (2023). Application of machine learning in personalized medicine. Intelligent Pharmacy, 1(3), 152–156. https://doi.org/10.1016/j.ipha.2023.06.004
16. Yılmaz, A. A. (2022a). A novel hyperparameter optimization aided hand gesture recognition framework based on deep learning algorithms. Traitement Du Signal, 39(3), 823–833. https://doi.org/10.18280/ts.390307
17. Yılmaz, A. A. (2022b). Intrusion detection in computer networks using optimized machine learning algorithms. 2022 3rd International Informatics and Software Engineering Conference (IISEC), 1–5. https://doi.org/10.1109/IISEC56263.2022.9998258
18. Yılmaz, A. A. (2025). A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks. PLOS One, 20(2), e0316253. https://doi.org/10.1371/journal.pone.0316253
19. Yılmaz, A. A., Güzel, M. S., Askerzade, İ., & Bostancı, E. (2020). A novel action recognition framework based on deep-learning and genetic algorithms. IEEE Access, 8, 100631–100644. https://doi.org/10.1109/ACCESS.2020.2997962
20. Zhang, M. L., & Zhou, Z. H. (2014). A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 26(8), 1819–1837. https://doi.org/10.1109/TKDE.2013.39