A Machine Learning Paradigm for Explanatory Cases with CKD

Chronic Kidney Disease (CKD) refers to the disorder in kidneys where it can't filter extra water and wastes out of the blood to make urine as normally. The infection is classified “chronic” in light of the fact that the harm to kidneys occurs gradually throughout a significant stretch of time. This harm can make squanders develop in the body. The cause for the CKD is mostly uncertain and there are several factors that influences in this disease, Among the many, the primary cause for this CKD are diabetes and hypertension. Both are liable for up to 66% of the cases other than that heart disease. It is conceivable to slow or stop the movement of kidney disease if it is properly treated in the early stages of infection/damage. In present research, we are trying to capture the explanatory cases from patient description and produce the explanations so that it can be easily understood by the experts as well as the layman. Usually, the predicted values that are obtained from any of the machine learning model are efficient. However, the results are opaque thus it requires an explanation for those cases like which factors are impacting for the disease, is achieved using LIME (Local interpretable model-agnostic explanations) algorithm.