• Umme Salma
• Mr. Rudresh N C
• Ayesha Siddiqua
• Sarah Itagi
• Shifa Noorain

Chronic kidney disease (CKD) is a common and serious condition that affects millions of people worldwide. Early identification of CKD can help prevent or delay its progression and improve patient outcomes. Machine learning (ML) algorithms have been increasingly used to predict CKD, but there is a need for more accurate and efficient models. This paper presents a comprehensive review of the literature on CKD prediction using ML techniques. We identified and analyzed the various features, datasets, ML algorithms, and evaluation metrics used in the studies. We also propose a novel approach that combines different feature selection and ML techniques to enhance CKD prediction accuracy. Our results show that ML algorithms, such as support vector machines, random forests, and neural networks, can achieve high accuracy in CKD prediction. Our proposed approach further improves the accuracy by up to 5% compared to existing methods. The findings of this study have important implications for the development of more accurate and efficient CKD prediction models that can be used in clinical practice to improve patient outcomes.

Chronic kidney disease (CKD), Machine learning (ML) algorithms, Early identification, Support vector machines, Random forests.

"CHRONIC KIDNEY DISEASE PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.k431-k435, April-2024, Available :http://www.jetir.org/papers/JETIR2404A57.pdf

"CHRONIC KIDNEY DISEASE PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppk431-k435, April-2024, Available at : http://www.jetir.org/papers/JETIR2404A57.pdf