Enhanced Predictive Modeling for Chronic Kidney Disease Diagnosis

To reduce adverse effects and unfavorable consequences, it is critical to appropriately diagnose chronic kidney disease (CKD), a medical illness that is becoming more and more common. In-depth investigation of predictive modeling techniques based on machine learning algorithms for the diagnosis of chronic renal disease is provided in this research paper. The study looks into the effectiveness of several algorithms, including Random Forest, Decision Tree, K-Nearest Neighbors (KNN), Gradient Boosting, Logistic Regression, XGBoost, and Support Vector Machine (SVM), to efficiently predict CKD based on patient data. We evaluate each algorithm's predictive power by doing a thorough examination of performance metrics, hyperparameter adjustment, and experimentation. There have been notable variations in performance parameters like as specificity, sensitivity, accuracy, and area under the Receiver Operating Characteristic (ROC) curve. The study also addresses the clinical practice implications of these findings, emphasizing how machine learning algorithms may help medical practitioners recognize and treat patients with CKD early. All things considered, this study offers insightful information about how to create predictive models for CKD diagnosis, which will help with better patient outcomes and healthcare decision-making.