Enhancing The Detection Of Chronic Kidney Disease Using Machine Learning by Denoising and Sparse Feature Approach with Biomarkers

Enhancing the detection of Chronic Kidney Disease (CKD) using machine learning involves applying denoising techniques and sparse feature selection to identify key biomarkers, improving early diagnosis accuracy. CKD poses several challenges due to the complex and multifactorial nature of the disease. Traditional diagnostic methods often fail to identify early-stage CKD, resulting in delayed intervention and poor patient outcomes. The study aims to create a reliable and precise machine-learning framework that leverages biomarker data to effectively classify patients with Chronic Kidney Disease (CKD). Collect blood and/or urine samples to measure a comprehensive panel of biomarkers, including Creatinine, glomerular filtration rate (GFR), and other standard clinical markers. Utilize unsupervised learning algorithms like Denoising Sparse Auto-Encoder (DSAE) to sift through medical data, looking for hidden patterns indicative of CKD. Apply Term Frequency - Inverse Document Frequency (TF-IDF) to identify the most important pieces of information in the data, focusing on key elements like changes in blood tests or abnormalities in scans crucial for CKD diagnosis. Implement Sequential Selection Ratio (SSR) and Feature Normalization Component (FNC) techniques for feature selection and normalization. Utilize an Adaptive Backpropagation Neural Network (ABPNN) as the main classification model, adjusting its learning rate during training to improve performance