• SIDDIK I
• Dr.K.N. ABDUL KADER NIHAL

Chronic Kidney Disease (CKD) poses a significant global health challenge, necessitating early detection and intervention to mitigate progression and associated complications. This study explores the application of Deep Learning (DL) approaches for CKD detection. We review various deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and ensemble methods, which have demonstrated promising results in analysing medical datasets. By employing datasets that include clinical parameters, laboratory results, and patient demographics. Our investigations/study indicate that DL methods can significantly improve CKD detection rates compared to traditional techniques, paving the way for the development of robust, scalable decision-support systems in clinical practice. This research underscores the potential of Artificial Intelligence (AI) in transforming kidney health management and facilitating timely interventions for patients with CKD.

Art-Risk Patients, DL, Risk Detection, Medical Datasets

"Chronic Kidney Disease (CKD) At-Risk Patients Detection Insights: A Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.d808-d815, November-2024, Available :http://www.jetir.org/papers/JETIR2411394.pdf

"Chronic Kidney Disease (CKD) At-Risk Patients Detection Insights: A Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppd808-d815, November-2024, Available at : http://www.jetir.org/papers/JETIR2411394.pdf