IJCCR_2025v15n5

International Journal of Clinical Case Reports, 2025, Vol.15, No.5, 209-218 http://medscipublisher.com/index.php/ijccr 217 Nascimento I.J., Marcolino M., Abdulazeem H., Weerasekara I., Azzopardi-Muscat N., Gonçalves M., and Novillo-Ortiz D., 2021, Impact of big data analytics on people’s health: overview of systematic reviews and recommendations for future studies, Journal of Medical Internet Research, 23(4): e27275. https://doi.org/10.2196/27275 Nenova Z., and Shang J., 2021, Chronic disease progression prediction: leveraging case-based reasoning and big data analytics, Production and Operations Management, 31: 259-280. https://doi.org/10.1111/poms.13532 Prosperi M., Min J., Bian J., and Modave F., 2018, Big data hurdles in precision medicine and precision public health, BMC Medical Informatics and Decision Making, 18(1): 139. https://doi.org/10.1186/s12911-018-0719-2 Rajeashwari S., and Arunesh K., 2024, Chronic disease prediction with deep convolution based modified extreme-random forest classifier, Biomed. Signal Process, Control, 87: 105425. https://doi.org/10.1016/j.bspc.2023.105425 Rashid J., Batool S., Kim J., Nisar M., Hussain A., and Kushwaha R., 2022, An augmented artificial intelligence approach for chronic diseases prediction, Frontiers in Public Health, 10: 860396. https://doi.org/10.3389/fpubh.2022.860396 Rehman A., Xing H., Hussain M., Gulzar N., Khan M., Hussain A., and Mahmood S., 2023, HCDP-DELM: Heterogeneous chronic disease prediction with temporal perspective enabled deep extreme learning machine, Knowl. Based Syst., 284: 111316. https://doi.org/10.1016/j.knosys.2023.111316 Rico J., Alaeddini A., Faruqui S., Fisher-Hoch S., and Mccormick J., 2024, A Laplacian regularized graph neural network for predictive modeling of multiple chronic conditions, Computer Methods and Programs in Biomedicine, 247: 108058. https://doi.org/10.1016/j.cmpb.2024.108058 Riley R.D., Ensor J., Snell K., Debray T., Altman D., Moons K., and Collins G., 2016, External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges., The BMJ, 353(i3140): i3140. https://doi.org/10.1136/bmj.i3140 Rong M., Gong D., and Gao X., 2019, Feature selection and its use in big data: challenges methods and trends, IEEE Access, 7: 19709-19725. https://doi.org/10.1109/ACCESS.2019.2894366 Santoso L., and Ma P., 2025, Comparative study of feature engineering techniques for predictive data analytics, Journal of Technology Informatics and Engineering, 3(2): 417-435. https://doi.org/10.51903/jtie.v3i2.225 Sawhney R., Malik A., Sharma S., and Narayan V., 2023, A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease, Decision Analytics Journal, 6: 100169. https://doi.org/10.1016/j.dajour.2023.100169 Snyder M., and Zhou W., 2019, Big data and health, The Lancet Digital Health, 1(6): e252-e254. https://doi.org/10.1016/s2589-7500(19)30109-8 Tsai H., Yang T., Wu T., Tu Y., Chen C., and Chou C., 2025, Multitask learning multimodal network for chronic disease prediction, Scientific Reports, 15(1): 15468. https://doi.org/10.1038/s41598-025-99554-z Tse G., Lee Q., Chou O., Chung C., Lee S., Chan J., Li G., Kaur N., Roever L., Liu H., Liu T., and Zhou J., 2023, Healthcare big data in Hong Kong: development and implementation of artificial intelligence-enhanced predictive models for risk stratification, Current problems in Cardiology, 49(1): 102168. https://doi.org/10.1016/j.cpcardiol.2023.102168 Uddin S., Haque I., Lu H., Moni M., and Gide E., 2022, Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction, Scientific Reports, 12(1): 6256. https://doi.org/10.1038/s41598-022-10358-x Verdonck T., Baesens B., Óskarsdóttir M., and Broucke S., 2021, Special issue on feature engineering editorial, Mach. Learn., 113: 3917-3928. https://doi.org/10.1007/s10994-021-06042-2 Yadav P., Member I., Mahadeva R., and Member I., 2023, Exploring hyper-parameters and feature selection for predicting non-communicable chronic disease using stacking classifier, IEEE Access, 11: 80030-80055. https://doi.org/10.1109/ACCESS.2023.3299332 Yang H., Chen Z., Yang H., and Tian M., 2023, Predicting coronary heart disease using an improved LightGBM model: performance analysis and comparison, IEEE Access, 11: 23366-23380. https://doi.org/10.1109/ACCESS.2023.3253885 Zhang S., Yang F., Wang L., Si S., Zhang J., and Xue F., 2023, Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model, PLOS Computational Biology, 19(9): e1011396. https://doi.org/10.1371/journal.pcbi.1011396 Zhang J., 2024, From genomic data to personalized medical decisions: challenges and opportunities, International Journal of Clinical Case Reports, 14(2): 107-116. https://doi.org/10.5376/ijccr.2024.14.0013

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