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

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Computers, Materials & Continua 2021年第5期67卷 1399-1412页

作者： Abdul Hannan Khan Muhammad Adnan Khan Sagheer Abbas Shahan Yamin Siddiqui Muhammad Aanwar Saeed Majed Alfayad Nouh Sabri Elmitwally School of Computer Sciences National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science&IT Minhaj University LahoreLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Virtual University of PakistanIslamabad45000Pakistan Department of Information Systems College of Computer and Information SciencesJouf UniversitySkakaAljouf72341Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversitySkakaAljouf72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo UniversityGiza12613Egypt

Artificial intelligence(AI)is expanding its roots in medical *** acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health *** 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 *** blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney ***,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 ***,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 *** the quick and accurate evaluation of the proposed system,Matlab version 2019 was *** proposed smoikd-FLSANN-deml expert system has shown 94.16%*** this study concluded that smoikd-FLS-ANN-deml system is effective to accurately diagnose kidney disease at initial levels.

关键词： Fuzzy logic system artificial neural network deep extreme machine learning feed-backward propagation smoikd-FLS smoikd-ANN smoikd-deml smoikd-FLS-ANN-deml

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