Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models

Author:

Alsekait Deema Mohammed1,Saleh Hager2ORCID,Gabralla Lubna Abdelkareim1,Alnowaiser Khaled3,El-Sappagh Shaker45ORCID,Sahal Radhya6ORCID,El-Rashidy Nora7ORCID

Affiliation:

1. Department of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

2. Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt

3. College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al Kharj 11942, Saudi Arabia

4. Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt

5. Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt

6. School of Computer Science and Information Technology, University College Cork, T12 R229 Cork, Ireland

7. Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt

Abstract

Chronic kidney disease (CKD) refers to the gradual decline of kidney function over months or years. Early detection of CKD is crucial and significantly affects a patient’s decreasing health progression through several methods, including pharmacological intervention in mild cases or hemodialysis and kidney transportation in severe cases. In the recent past, machine learning (ML) and deep learning (DL) models have become important in the medical diagnosis domain due to their high prediction accuracy. The performance of the developed model mainly depends on choosing the appropriate features and suitable algorithms. Accordingly, the paper aims to introduce a novel ensemble DL approach to detect CKD; multiple methods of feature selection were used to select the optimal selected features. Moreover, we study the effect of the optimal features chosen on CKD from the medical side. The proposed ensemble model integrates pretrained DL models with the support vector machine (SVM) as the metalearner model. Extensive experiments were conducted by using 400 patients from the UCI machine learning repository. The results demonstrate the efficiency of the proposed model in CKD prediction compared to other models. The proposed model with selected features using mutual_info_classi obtained the highest performance.

Funder

Nourah bint Abdulrahman University Researchers

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference76 articles.

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2. Epidemiology of chronic kidney disease: An update 2022;Kovesdy;Kidney Int. Suppl.,2022

3. Zhou, Y., and Yang, J. (2020). Chronic Kidney Disease, Springer.

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