Using Ensemble Learning and Advanced Data Mining Techniques to Improve the Diagnosis of Chronic Kidney Disease

Abstract

Kidney failure is a condition with far-reaching, potentially life-threatening consequences on the human body. Leveraging the power of machine learning and data mining, this research focuses on precise disease prediction to equip decision-makers with critical data-driven insights. The accuracy of classification systems hinges on the dataset's inherent characteristics, prompting the application of feature selection techniques to streamline algorithm models and optimize classification precision. Various classification methodologies, including K-Nearest Neighbor, J48, Artificial Neural Network (ANN), Naive Bayes, and Support Vector Machine, are employed to detect chronic renal disease. A predictive framework is devised, blending ensemble methods with feature selection strategies to forecast chronic kidney disease. Specifically, the predictive model for chronic kidney disease is meticulously constructed through the fusion of an information gain-based feature evaluator and a ranker search mechanism, fortified by the wrapper subset evaluator and the best first algorithm. J48, in tandem with the Info Gain Attribute Evaluator and ranker search system, exhibits a remarkable accuracy rate of 97.77%. The Artificial Neural Network (ANN), coupled with the Wrapper Subset Evaluator and the highly effective Best First search strategy, yields precise results at a rate of 97.78%. Similarly, the Naive Bayes model, when integrated with the Wrapper Subset Evaluator (WSE) and the Best First search engine, demonstrates exceptional performance, achieving an accuracy rate of 97%. Furthermore, the Support Vector Machine algorithm achieves a notable accuracy rate of 97.12% when utilizing the Info Gain Attribute Evaluator. The K-Nearest Neighbor Classifier, in conjunction with the Wrapper Subset Evaluator, emerges as the most accurate among the foundational classifiers, boasting an impressive prediction accuracy of 98%. A second model is introduced, incorporating five diverse classifiers operating through a voting mechanism to form an ensemble model. Investigative findings highlight the efficacy of the proposed ensemble model, which attains a precision rate of 98.85%, as compared to individual base classifiers. This research underscores the potential of combining feature selection and ensemble techniques to significantly enhance the precision and accuracy of chronic kidney disease prediction.