Machine Learning Prediction Models for Chronic Kidney Disease with Imbalanced Data.

Music City Center 

Chronic kidney disease (CKD) is a disease characterized by the gradual decline of kidney function over time due to which kidneys slowly lose their ability to filter waste and excess fluid from the blood. This causes a buildup of toxin in the body leading to kidney failure (End-Stage Renal Disease, ESRD) and other serious health complications. The prevalence of CKD has become a major concerning public health issue globally and in the United States, with its prevalence steadily increasing over the years. Early detection of CKD is crucial for effective management and treatment so that the progression of ESRD can be prevented or delayed thus reducing the overly expensive treatment cost. In this paper, we explore the use of machine learning (ML) techniques to predict Chronic Kidney Disease (CKD) based on the South Carolina Behavioral Risk Factor Surveillance System (BRFSS) dataset. However, the dataset is imbalanced, with a much smaller number of CKD cases compared to healthy individuals. The study compares different ML algorithms and tackles the challenges of imbalanced data.

Machine Learning

Chronic kidney disease

imbalanced data

South Carolina

BRFSS 

Section on Statistical Learning and Data Science