Validation of Sophisticated Machine Learning Model for Early Detection of Acute Kidney Injury

To improve diagnostic precision and aid in early detection initiatives, this work suggests a stable ensemble model for the prediction of chronic kidney disease (CKD). By integrating the predictive capabilities of Gradient Boosting, Random Forest, and Logistic Regression techniques, our ensemble model attains an impressive 98% accuracy rate. Compared to solo models, the integration of these many algorithms performs better because it uses their particular strengths and makes up for their shortcomings. The interpretability and robustness of Logistic Regression add to the model in the presence of linear connections, whereas Random Forest reduces overfitting by introducing randomization through bootstrapping. Gradient Boosting improves the prediction even further by gradually fixing mistakes made by earlier predictors. The combination of these techniques guarantees a thorough examination of the intricate patterns present in CKD datasets. In addition to showing excellent accuracy, the suggested model also exhibits good precision and recall, indicating that it may prove to be a useful tool in clinical settings for the prompt and efficient management of CKD.