Deep Prediction Of Chronic Kidney Disease

Abstract

Chronic Kidney Disease (CKD) affects millions globally and early detection is crucial for preventing progression and complications. The use of Deep Learning algorithms to analyze massive volumes of medical data in search of patterns and correlations has great potential for the prediction of CKD using demographic, clinical, and laboratory outcomes. A Deep Learning model for forecasting CKD according to these characteristics is suggested for development in this work. This model will provide a rapid and accurate tool for early identification and effective treatment of the illness. With its reliable predictions, the suggested approach shows great promise as a tool to enhance CKD identification and treatment. Around $12 billion will be needed to treat all existing and future cases of renal failure in Australia until 2020. An effective method for early-stage CKD prediction is provided by machine learning techniques.