Chronic kidney disease prediction model using machine learning approach

Munusamy Chitra (Perunthalaivar Kamarajar Arts College)
Abdul Kuthus Parveen (Perunthalaivar Kamarajar Arts College)
Murugadoss Elavarasi (Perunthalaivar Kamarajar Arts College)
Jayamoorthy Sangeetha (Perunthalaivar Kamarajar Arts College)
Ramalingam Vaittilingame (Perunthalaivar Kamarajar Arts College)

International Journal of Informatics and Communication Technology (IJ-ICT)
Vol 12, No 2: August 2023

Chronic disease (CD) such as kidney disease and causes severe challenging issues to the people all around the world. Chronic kidney disease (CKD) and diabetes mellitus (DM) are considered in this paper. Predicting the diseases in earlier stage, gives better preventive measures to the people. Healthcare domain leads to tremendous cost savings and improved health status of the society. The main objective of this paper is to develop an algorithm to predict CKD occurrence using machine learning (ML) technique. The commonly used classification algorithms namely logistic regression (LR), random forest (RF), conditional random forest (CRF), and recurrent neural networks (RNN) are considered to predict the disease at an earlier stage. The proposed algorithm in this paper uses medical code data to predict disease at an earlier stage.