Chronic Kidney Disease Detection Using Machine Learning: From Analysis to Framework Development

Abstract

Considering the aspects of sustainable development goals, Good health and well-being ensure the development of a nation. Chronic kidney disease (CKD) is a progressive and irreversible condition characterized by the gradual loss of kidney function over time. One of the major diseases, CKD affecting 10-15% population globally needs to be detected at early stages to reduce morbidities and mortalities. Majorly the risk factors include Diabetes, Hypertension, Age, Hereditary, and Ethnicity which need to be screened on regular intervals to ensure the timely detection of the disease. The primary hurdle for detection is asymptomatic behavior during the early stages. Machine learning (ML) based models are majorly governing various sectors and applications. The models have capabilities to serve as assistance to the medical practitioners for effective CKD detection at early stages. This paper demonstrates the development of a framework for early detection considering various parameters.