Prediction of Chronic Kidney Disease Using Deep Neural Network

TL;DR

This paper applies a deep neural network to predict chronic kidney disease using patient data, achieving high accuracy and identifying key features influencing the prediction.

Contribution

It introduces a DNN-based approach for CKD prediction with a real dataset, highlighting feature importance for better diagnosis.

Findings

DNN achieved 98% accuracy in CKD prediction.

Creatinine and Bicarbonate are the most influential features.

The model provides a reliable tool for early CKD detection.

Abstract

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.