Hybrid classification framework for chronic kidney disease prediction model

Abstract

ABSTRACT ‘Chronic kidney disease (CKD) – or chronic renal failure (CRF) is a term that encompasses all degrees of decreased kidney function, from damaged–at risk through mild, moderate, and severe chronic kidney failure’. As a risky factor, the disease has steadily turned out to be a major cause of death and morbidity. Accordingly, ultrasound (US) is significant in enhancing the rates of early recognition of CKD. Here, a new CKD detection model is introduced that includes ‘(1) Pre-processing (2) segmentation (3) Feature extraction, and (4) Classification’. Improved Gaussian filtering is used for pre-processing, and watershed-based segmentation is carried out. Additionally, features like the ROI, mean intensity, and the projected Local Vector Pattern (LVP) are retrieved. The ‘Optimized Neural Network (NN) and Long Short-Term Memory (LSTM)’ are then provided the output of the features. Additionally, using Self Updated Cat Swarm Optimization, the weights of NN are adjusted in order to increase the classifier's prediction accuracy (SU-CSO). The categorized output is then calculated by averaging the results from the improved NN and LSTM. Lastly, it is demonstrated that the proposed strategy is superior to other options.