Selective Partitioned Regression for Accurate Kidney Health Monitoring.

ABSTRACT: The number of people diagnosed with advanced stages of kidney disease have been rising every year. Early detection and constant monitoring are the only minimally invasive means to prevent severe kidney damage or kidney failure. We propose a cost-effective machine learning-based testing system that can facilitate inexpensive yet accurate kidney health checks. Our proposed framework, which was developed into an iPhone application, uses a camera-based bio-sensor and state-of-the-art classical machine learning and deep learning techniques for predicting the concentration of creatinine in the sample, based on colorimetric change in the test strip. The predicted creatinine concentration is then used to classify the severity of the kidney disease as healthy, intermediate, or critical. In this art

SUBMITTER: Whelan A 

PROVIDER: S-EPMC10995075 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature