Open Access

ARTICLE

Simulation, Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches

by Abdul Hannan Khan^1,2, Muhammad Adnan Khan^3,*, Sagheer Abbas², Shahan Yamin Siddiqui^1,2, Muhammad Aanwar Saeed⁴, Majed Alfayad⁵, Nouh Sabri Elmitwally^6,7

1 School of Computer Sciences, National College of Business Administration and Economics, Lahore, 54000, Pakistan
2 Department of Computer Science & IT, Minhaj University Lahore, Lahore, 54000, Pakistan
3 Department of Computer Science, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan
4 Department of Computer Science, Virtual University of Pakistan, Islamabad, 45000, Pakistan
5 Department of Information Systems, College of Computer and Information Sciences, Jouf University, Skaka, Aljouf, 72341, Saudi Arabia
6 Department of Computer Science, College of Computer and Information Sciences, Jouf University, Skaka, Aljouf, 72341, Saudi Arabia
7 Department of Computer Science, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, 12613, Egypt

* Corresponding Author: Muhammad Adnan Khan. Email:

Computers, Materials & Continua 2021, 67(2), 1399-1412. https://doi.org/10.32604/cmc.2021.012737

Received 30 August 2020; Accepted 16 December 2020; Issue published 05 February 2021

Abstract

Artificial intelligence (AI) is expanding its roots in medical diagnostics. Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications. Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure. High blood pressure, diabetes mellitus, and glomerulonephritis are the root causes of kidney disease. Therefore, the present study is proposed a set of multiple techniques such as simulation, modeling, and optimization of intelligent kidney disease prediction (SMOIKD) which is based on computational intelligence approaches. Initially, seven parameters were used for the fuzzy logic system (FLS), and then twenty-five different attributes of the kidney dataset were used for the artificial neural network (ANN) and deep extreme machine learning (DEML). The expert system was proposed with the assistance of medical experts. For the quick and accurate evaluation of the proposed system, Matlab version 2019 was used. The proposed SMOIKD-FLS-ANN-DEML expert system has shown 94.16% accuracy. Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.

---

Keywords

---

Citations