The gradual decline of kidney function in chronic kidney disease (CKD) continues to be a significant public health problem, with many patients advancing to end stage renal disease and being placed on long-term hemodialysis. Timely prediction of chronic hemodialysis precondition is quite important for early clinical decision and patient management. We propose an application-centred predictive modeling framework for chronic hemodialysis that considers high-dimensional multiclass clinical data sets. Different from traditional CKD diagnosis models predicting a binary outcome, our method considers multi-class risk stratification and real-time decision support in a deployable clinical APP platform.
The framework utilizes complex nonlinear feature selection to extract a subset of relevant features including both numerical and categorical types, while eliminating redundancy and boosting interpretability. Hybrid statistical and machine learning methods for feature ranking are used to select relevant biomarkers for the dialysis outcome. Several supervised learning algorithms are utilized and tested for their performance in terms of prediction accuracy, robustness and generalization ability on various sets of patients records. To close the gap between theoretical modeling and clinical practical use, a web-based tool is developed to provide real-time hemodialysis risk prediction for clinicians. The system receives the patient clinical parameters as inputs and delivers multiclass risk estimation to facilitate prescriptive treatment strategies. Performance comparison in a large-scale clinical dataset experimental evaluation shows enhanced classification performance with respect to accuracy, Precision, Recall, and F1-score, as well as stability when comparing to traditional single-model schemes.
Keywords: Imbalanced hemodialysis data, probabilistic clustering, support vector machine, decision tree, ensemble approach model.
How to cite this article: Hemalatha T, Kiran KVD, An Application-Centric Predictive Modeling Approach for Chronic Hemodialysis Using High-Dimensional Multiclass Clinical Data. Int J Drug Deliv Technol. 2026;16(4s): 228-240; DOI: 10.25258/ijddt.16.228-240