  Deep neural Network (DNN) is becoming a focal point in Machine Learning
research. Its application is penetrating into different fields and solving
intricate and complex problems. DNN is now been applied in health image
processing to detect various ailment such as cancer and diabetes. Another
disease that is causing threat to our health is the kidney disease. This
disease is becoming prevalent due to substances and elements we intake. Death
is imminent and inevitable within few days without at least one functioning
kidney. Ignoring the kidney malfunction can cause chronic kidney disease
leading to death. Frequently, Chronic Kidney Disease (CKD) and its symptoms are
mild and gradual, often go unnoticed for years only to be realized lately.
Bade, a Local Government of Yobe state in Nigeria has been a center of
attention by medical practitioners due to the prevalence of CKD. Unfortunately,
a technical approach in culminating the disease is yet to be attained. We
obtained a record of 400 patients with 10 attributes as our dataset from Bade
General Hospital. We used DNN model to predict the absence or presence of CKD
in the patients. The model produced an accuracy of 98%. Furthermore, we
identified and highlighted the Features importance to provide the ranking of
the features used in the prediction of the CKD. The outcome revealed that two
attributes; Creatinine and Bicarbonate have the highest influence on the CKD
prediction.
