International Journal of Clinical Case Reports, 2025, Vol.15, No.5, 209-218 http://medscipublisher.com/index.php/ijccr 216 The new development trends include the use of reinforcement learning to support real-time adaptive decision-making and the utilization of causal reasoning to identify the true causes of diseases, rather than just correlations. To apply these advanced methods in clinical practice, it is also necessary to ensure that the models have sufficient interpretability, guarantee data security, and be consistent with the clinical workflow, so that the prediction tools are both reliable and acceptable and widely promoted by doctors. Acknowledgments The author extends sincere thanks to two anonymous peer reviewers for their feedback on the manuscript. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Akter S., Habib A., Islam M., Hossen M., Fahim W., Sarkar P., and Ahmed M., 2021, Comprehensive performance assessment of deep learning models in early prediction and risk identification of chronic kidney disease, IEEE Access, 9: 165184-165206. https://doi.org/10.1109/ACCESS.2021.3129491 Alam M., Sohel A., Uddin M., and Siddiki A., 2024, Big data and chronic disease management through patient monitoring and treatment with data analytics, Academic Journal on Science Technology, Engineering and Mathematics Education, 1(1): 77-94. https://doi.org/10.69593/ajaimldsmis.v1i01.133 Al-Jamimi H., 2024, Synergistic feature engineering and ensemble learning for early chronic disease prediction, IEEE Access, 12: 62215-62233. https://doi.org/10.1109/ACCESS.2024.3395512 Alonso S., Díez I., Rodrigues J., Hamrioui S., and Coronado M., 2017, A systematic review of techniques and sources of big data in the healthcare sector, Journal of Medical Systems, 41: 1-9. https://doi.org/10.1007/s10916-017-0832-2 Battineni G., Sagaro G., Chinatalapudi N., and Amenta F., 2020, Applications of machine learning predictive models in the chronic disease diagnosis, Journal of Personalized Medicine, 10(2): 21. https://doi.org/10.3390/jpm10020021 Chen M., Hao Y., Hwang K., Wang L., and Wang L., 2017, Disease prediction by machine learning over big data from healthcare communities, IEEE Access, 5: 8869-8879. https://doi.org/10.1109/ACCESS.2017.2694446 Chicco D., Oneto L., and Tavazzi E., 2022, Eleven quick tips for data cleaning and feature engineering, PLOS Computational Biology, 18(12): e1010718. https://doi.org/10.1371/journal.pcbi.1010718 Chittora P., Chaurasia S., Chakrabarti P., Kumawat G., Chakrabarti T., Leonowicz Z., Jasinski M., Jasiński Ł., Goňo R., Jasińska E., and Bolshev V., 2022., Prediction of chronic kidney disease-a machine learning perspective, IEEE Access, 9: 17312-17334. https://doi.org/10.1109/ACCESS.2021.3053763 Christ M., Braun N., Neuffer J., and Kempa-Liehr A., 2018, Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh-A Python package), Neurocomputing, 307: 72-77. https://doi.org/10.1016/J.NEUCOM.2018.03.067 Delpino F., Costa A., Farias S., Filho A., Arcêncio R., and Nunes B., 2022, Machine learning for predicting chronic diseases: a systematic review, Public Health, 205: 14-25. https://doi.org/10.1016/j.puhe.2022.01.007 Feng R., Cao Y., Liu X., Chen T., Chen J., Chen D., Gao H., and Wu J., 2021, ChroNet: a multi-task learning based approach for prediction of multiple chronic diseases, Multimedia Tools and Applications, 81: 41511-41525. https://doi.org/10.1007/s11042-020-10482-8 Ghosh S., and Khandoker A., 2024, Investigation on explainable machine learning models to predict chronic kidney diseases, Scientific Reports, 14: 3687. https://doi.org/10.1038/s41598-024-54375-4 Khalid H., Khan A., Khan M., Mehmood G., and Qureshi M., 2023, Machine learning hybrid model for the prediction of chronic kidney disease, Computational Intelligence and Neuroscience, 2023(1):9266889. https://doi.org/10.1155/2023/9266889 Kim G., Lim H., Kim Y., Kwon O., and Choi J., 2023, Intra-person multi-task learning method for chronic-disease prediction, Scientific Reports, 13(1): 1069. https://doi.org/10.1038/s41598-023-28383-9 Liu M., Zhou J., Xi Q., Liang Y., Li H., Liang P., Guo Y., Liu M., Temuqile T., Yang L., and Zuo Y., 2023, A computational framework of routine test data for the cost-effective chronic disease prediction, Briefings in Bioinformatics, 24(2): bbad054. https://doi.org/10.1093/bib/bbad054 Ngiam K., and Khor I., 2019, Big data and machine learning algorithms for health-care delivery, The Lancet Oncology, 20(5): e262-e273. https://doi.org/10.1016/S1470-2045(19)30149-4
RkJQdWJsaXNoZXIy MjQ4ODYzNA==