• V.Sujatha
• Naresh

ABSTRACT In today's society, people are increasingly striving to maintain a higher level of awareness regarding their health. However, Chronic Kidney Disease (CKD) poses a unique challenge as it may not exhibit any symptoms or may require specific disease-related indicators, making it difficult to predict or anticipate its occurrence. Previous literature has utilized classification and regression techniques such as SVM and RF, but these methods have limitations due to the limited time frame considered. To address these limitations, we are implementing clustering techniques such as K-means in conjunction with Principal Component Analysis (PCA). The accuracy rate of estimating CKD using K-means is 99%. This study evaluates the prediction of renal disease using K-means in clustering. The results provide support for increased accuracy in the early categorization of chronic kidney disease.

: (CKD)chronic kidney disease, (SVM) Support Vector Machines, (RF) Random Forest.

"PREDICTION OF CHRONIC KIDNEY DISEASES USING CLUSTERING TECHNIQUE IN MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.k633-k641, June-2022, Available :http://www.jetir.org/papers/JETIR2206A88.pdf

"PREDICTION OF CHRONIC KIDNEY DISEASES USING CLUSTERING TECHNIQUE IN MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppk633-k641, June-2022, Available at : http://www.jetir.org/papers/JETIR2206A88.pdf