PREDICTION OF CHRONIC KIDNEY DISEASE USING DEEP NEURAL NETWORK - Ibrahim Iliyas, Iliyas; Rambo, Saidu Isah; Dauda, Ali Baba; Tasiu, Suleiman (FUDMA JOURNAL OF SCIENCES, 2021)
eural Network (DNN) is now applied in disease prediction to detect various ailments such as heart disease and diabetes. Another disease that is causing a threat to our health is kidney disease. This disease is becoming prevalent due to substances and elements we intake. 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 of late. We conducted our research on CKD in Bade, a Local Government Area of Yobe State in Nigeria. The area 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 1200 patients with 10 attributes as our dataset from Bade General Hospital and used the DNN model to predict CKD’s absence or presence in the patients. The model produced an accuracy of 98%. Furthermore, we identified and highlighted the Features importance to rank the features used in predicting the CKD. The outcome revealed that two attributes: Creatinine and Bicarbonate, have the highest influence on the CKD prediction.