Hybrid machine learning model to predict chronic kidney diseases using handcrafted features for early health rehabilitation

Abstract

Chronic kidney diseases proliferate due to hypertension, diabetes, anemia, obesity, smoking etc. Patients with such conditions are sometimes unaware of first symptoms, complicating disease diagnosis. This paper presents chronic kidney disease (CKD) prediction model to classify CKD patients from NCKD (Non-CKD). The proposed study has two main stages. First, we found the odds ratio through logistic regression and comparison test to identify early risk factors from kidneys? MRI and differentiate CKD from NCKD subjects. In stage 2, LR, LDA, MLP classifiers were applied to predict CKD and NCKD by extracting features from MRI. The odds ratio of blood glucose random and serum creatinine was found higher, and levels of sodium, Potassium, packed cell volume, white blood cell count, and red blood cell count were found lesser in CKD. The comparison results show increase levels in blood glucose random, serum creatinine and decreased levels found in sodium, potassium, packed cell volume, White blood cell and red blood cell count respectively in CKD patients than NCKD subjects. The accuracies of LR were 98.5% and 97.5% for train & test datasets. While LDA accuracy was 96.07% and 96.6% for train and test datasets. Likewise, MLP attained were 95% and 94.1% accuracy for train and test datasets. Finally, we used 5-fold CV approach on the LR model. The mean accuracies of LR were 0.954 and 0.942 for training and testing data respectively. According to LR the serum creatinine, Albumin, Diabetes mellitus, red blood cells count, pus cell and hypertension were found to be the most significant features to discriminate the CKD patients from NCKD. The proposed strategy is best suited for practical implementation for reducing the disease's prevalence.