Hybrid classification framework for chronic kidney disease prediction model

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.

KEYWORDS:

• Chronic kidney
• Proposed LVP
• LSTM
• Optimized NN
• SU-CSO Algorithm
• Prediction
• Region of Interest
• Features

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Smitha Patil

Mrs. Smitha Patil received the B.E. degree in Information Science & Engineering from P.D.A College of Engineering Gulbarga, Karnataka and M.Tech in Computer Science & Engg. from VTU PG Centre Gulbarga, Karnataka and currently working as Assistant Professor in the Dept. of Computer Science & Engineering, Presidency University Bangalore, Karnataka. Her research interest areas are Machine Learning, Data Mining, and Deep Learning.

Savita Choudhary

Dr. Savita Choudhary received the B.E degree in Computer Science & Engineering from MITS, Lakhmangrah, Rajasthan, M. Tech in Software Engineering from Bansthali Vidhypith, Rajasthan and Ph.D. from BU, Rajasthan and currently working as Associate professor in the Department of Computer Science & Engineering, SIR MVIT, Bangalore, Karnataka. Her research interest areas are Machine Learning, Data Mining, and Deep Learning, Soft Computing, with more than 40 articles published.