A Feature Selection-Based Ensemble Machine Learning Method for Predicting Chronic Kidney Cancer

Chronic kidney cancer (CKC) is a real-world example of a crucial class of frontiers that are behind basic medical diagnostics and have a significant worldwide impact. For this reason, we need to evaluate more sophisticated prediction algorithms. In order to balance the previously proven CKC detection, this research has presented a novel strategy that combines ensemble approaches with robust machine learning. We employ nine alternative algorithms three from each of Random Forest, Decision Tree, and Light Gradient Boosting Machine in addition to the standard predictive models for this specific stacking ensemble. We obtained an accuracy of 99.19% after preprocessing the data and using collaboration for feature selection. Our model performs with excellent predictive power, similar to Random Forests, but with the ease of de-biasing and good interpretability of Decision Trees. Additionally, as a mixed category, the ensemble performs better than any one model on all performance criteria (precision, recall, and F1 score). In order to enhance individual risk prediction of CKC and intervention execution, this adaptable framework is therefore ideal for use in clinical settings. Let’s put it this way: our study is a significant step toward improving CKC diagnosis, and it provides a foundation for the broader application of ensemble models of learning in healthcare, which might revolutionize patient care in all specializations.