• Somnath Suresh Shinde
• Ganesh Jayant Kaldhone
• Atharv Arvind Deshpande
• Jeevan Bajrang Koli
• Shreyas Shankar Surwase

Chronic Kidney Disease (CKD) has become a major health concern worldwide, affecting millions of people and often leading to fatal outcomes. Early detection and accurate prediction of CKD are crucial for timely intervention and effective management. In this study, we propose a machine learning-based prediction system for CKD, leveraging clinical and demographic data to predict the risk of CKD onset. The system utilizes a dataset comprising comprehensive clinical features, including age, gender, blood pressure, serum creatinine, and other relevant parameters. We explore various machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), and Logistic Regression, to develop and compare the performance of our prediction model. The dataset was preprocessed, including handling missing values, normalization, and feature selection, to ensure optimal model performance. Evaluation metrics, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC), were employed to assess the models' performance. Our results indicate that the Random Forest algorithm outperforms other models with an accuracy of over 95% and an AUC-ROC of 0.98. The developed prediction system demonstrates promising results in identifying individuals at risk of developing CKD. The proposed system could be integrated into clinical practice to assist healthcare professionals in making informed decisions, leading to early detection and timely intervention, thereby improving patient outcomes and reducing the burden on healthcare systems.

– Chronic Kidney Disease (CKD), Machine Learning, Prediction System, Random Forest, Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Logistic Regression, Clinical Data, Early Detection, Healthcare, Risk Prediction, Feature Selection, Model Evaluation, Accuracy, Sensitivity, Specificity, Receiver Operating Characteristic Curve (ROC), Healthcare Management.

"Chronic Kidney Disease Prediction using Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l552-l558, April-2024, Available :http://www.jetir.org/papers/JETIR2404B73.pdf

"Chronic Kidney Disease Prediction using Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl552-l558, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B73.pdf