Design of chronic kidney disease prediction model on imbalanced data using machine learning techniques

Abstract

The objective of this paper is to build a CKD prediction model using machine learning techniques that can predict the risk of chronic kidney disease (CKD) in patients with Cardiovascular Disease (CVD) or at high risk of CVD. CVD is associated with worsening of renal functions. But patients with CVD remains often underdiagnosed and undertreated for CKD because mostly the clinical diagnosis and treatment are single organ centered in earlier stages. Machine learning algorithms have been widely used to predict and classify diseases in healthcare. Healthcare data is often imbalanced. In this analysis, the CKD prediction model is built using CVD data with imbalanced distribution of positive and negative cases. The analysis involves three stages: Stage I involves selecting the best model based on performance metrics that support imbalanced class distribution without applying any resampling techniques. Stage II involves oversampling the training data of the minority class using Synthetic Minority Oversampling Technique (SMOTE) and stage III involves randomly under-sampling the training data of the majority class to solve the class imbalance. The experimental results show that the MLP (Multi-Layer Perceptron)-SMOTE model performs better in predicting CKD with a better F-score, recall, precision, G-mean, balanced accuracy and RUC-AUC when compared to other models.