A Lite-Weight Clinical Features Based Chronic Kidney Disease Diagnosis System Using 1D Convolutional Neural Network

Chronic kidney disease (CKD) is heterogeneous disorders that affects the renal functions and structures of millions of people around the globe, and it is one of the leading causes of morbidity and mortality. Given the circumstances, several studies had been conducted in order to detect CKD at an early stage. However, each of these studies has its own set of limitations such as the failure to employ proper methods for coping with missing values, anomalies, and class imbalance problems, overfitting issues, and so on. Taking into account the shortcomings that recent research has uncovered, we propose a novel CKD diagnosis method based on 1D Convolutional Neural Network (1D CNN) that overcomes the aforementioned drawbacks while also significantly improving diagnosis accuracy. The Chronic Kidney Diseases Dataset from the UCI Machine Learning Repository has been used in this study. MissForest imputation, a precise non-parametric missing value imputation process, has been used to handle missing data. Additionally, memory-efficient Isolation Forest has been applied to deal with anomalies. After evaluating the model with chronic kidney disease dataset, our proposed model achieved 99.21 % accuracy which is better than the state of the art method.