Chronic Kidney Disease Prediction Using ML-Based Neuro-Fuzzy Model

Abstract

Nowadays, in most countries, the most dangerous and life threatening infection is Chronic Kidney Disease (CKD). A progressive malfunctioning of the kidneys and less effectiveness of the kidney are considered CKD. CKD can be a life threatening disease if it continues for longer period of time. Prediction of chronic disease in early stage is very crucial so that sustainable care of the patient is taken to prevent menacing situations. Most of the developing countries are being affected by this deadly disease and treatment applied for this disease is also very expensive, here in this paper, a Machine Learning (ML)-positioned approach called Neuro-Fuzzy model is used for prediction belonging to CKD. Based on the image processing technique, fibrosis proportions are detected in the kidney tissues. It also builds a system for identifying and detection of CKD at an early stage. Neuro-Fuzzy model is based on ML which can detect risk of CKD patients. Compared with other conventional methods such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), the proposed method of this paper — ML-based Neuro-Fuzzy logic method — obtained 97% accuracy in CKD prediction. This method can be evaluated based on various parameters such as Precision, Accuracy, Recall and F1-Score in CKD prediction. From the results, the patients having high risk of chronic disease can be predicted.