Predicting chronic kidney disease using ML by using two different datasets: A comparison study

The kidneys, filter waste products from the blood, produce urine, and a hormone that is important for the red blood cells, blood pressure regulation, and calcium metabolism. When the functioning of the kidneys gradually declines over time, Chronic Kidney Disease emerges. Glomerular Filtration Rate is a strong indicator of this disease which can be effectively predicted by using Machine Learning techniques. In this study, the performances of eight Machine Learning models and an ensemble of them are compared using two different public datasets and a combined dataset of these two. One of the datasets is from India with 400 records, and 26 attributes from the year 2015, and the other dataset is from Bangladesh with 200 records and 29 attributes from the year 2021. Cross-checking is performed using the eight models and an ensemble of them, such that the nine models trained on one dataset are tested on the other dataset which is unseen by the models. The nine models are trained and tested on a combined dataset of the two. Also, the models are used on a reduced set of features of the combined dataset.