Chronic Kidney Disease Detection Using Machine Learning: From Analysis to Framework Development
Bobbinpreet Kaur1*, Bhawna Goyal1
and Ayush Dogra2, Sonam Ramshankar3, Devendra Singh4 and Ahmed Alkhayyat51ECE, Chandigarh University, Mohali, India.
2Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India
3IES Institute of Pharmacy, IES University, Bhopal, Madhya Pradesh India 462044
4Uttaranchal Institute of Technology, Uttaranchal University, Dehradun-248007, India
5College of Technical Engineering, The Islamic University, Najaf, Iraq
Corresponding Author E-mail: ayush123456789@gmail.com
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.
Keywords: CKD; Ensemble learning; Good Health; Improving Mortality; Machine Learning; Medical assistance Well-being Back to TOC