A Novel Technique for Chronic Kidney Disease Prediction using Glowworm Swarm Algorithm with Adaptive Neuro Fuzzy Inference System

Anindita Khade; Dhawal Powle; Gaurav Keshari

doi:10.35882/jeeemi.v7i2.623

Anindita Khade Department of Computer Engineering, SVKM'S Narsee Monjee Institute of Management Studies Deemed to be University, Navi Mumbai, Maharashtra, India https://orcid.org/0000-0003-2616-5092
Dhawal Powle Department of Artificial Intelligence and Data Science, SVKM'S Narsee Monjee Institute of Management Studies Deemed to be University, Navi Mumbai, Maharashtra, India https://orcid.org/0009-0005-8304-9268
Gaurav Keshari Department of Artificial Intelligence and Data Science, SVKM'S Narsee Monjee Institute of Management Studies Deemed to be University, Navi Mumbai, Maharashtra, India. https://orcid.org/0009-0003-0583-8902

DOI: https://doi.org/10.35882/jeeemi.v7i2.623

Keywords: ABPNN-ANFIS, DL Algorithms, UCI CKD Dataset, GSO, TS , CKD.

Abstract

The term chronic kidney disease (CKD) describes the progressive loss of kidney function; according to recent studies, the incidence of this ailment is increasing yearly. The great accuracy of machine learning approaches in diagnosing chronic kidney disease has made them more important in medical diagnosis. More recently, efforts have been made to optimize these methods by using efficient feature selection algorithms with the goal of minimizing dataset dimensionality. This research suggests using an advanced feature selection technique using Tabu Search (TS) based Stochastic Diffusion Search (SDS). 19 characteristics were chosen and 5 features were eliminated after using this method. When it comes to diagnosing CKD, the proposed Adaptive Neuro Fuzzy Inference System (ANFIS) has outperformed other state of art machine learning techniques. Through the use of an enhanced diagnostic technique utilizing the glowworm swarm optimization algorithm (GSO), this work improves the ANFIS model. By simulating glowworm behavior during food hunting, this global optimization technique increases ANFIS efficiency. Furthermore, to improve the convergence speed during network training, the suggested method incorporates a hybrid learning algorithm that combines the conjugate gradient descent and the Least Square Estimator (LSE). Fuzzy logic is added to the Adaptive Backpropagation Neural Network (ABPNN) classifier to boost performance. Findings highlight the effectiveness of the ABPNN-GSO-ANFIS in diagnosing CKD, with 99.52% accuracy, 99.34% precision, and 97.82% recall achieved.

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Author Biographies

Anindita Khade, Department of Computer Engineering, SVKM'S Narsee Monjee Institute of Management Studies Deemed to be University, Navi Mumbai, Maharashtra, India

Chronic Kidney Disease (CKD) describes the gradual reduction in kidney function. According to reports published by the World Health Organization (WHO), the prevalence of this disease in Indian adults is comparatively high. The data indicate that 220,000 new patients need renal replacement therapy in India annually. The enhanced precision of Machine Learning (ML) methods in the diagnosis of CKD has made them more significant in medical diagnostics. In recent times, attempts have been made to enhance these methods with the use of effective feature selection algorithms for dataset dimensionality reduction. This research suggests the use of an enhanced feature selection method combining Tabu Search (TS) and Stochastic Diffusion Search (SDS). Following the use of this method, five of the 24 features were removed. In the diagnosis of CKD, the proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) has performed better than other state-of-the-art ML methods. With the aid of an improved diagnostic technique that employs the glowworm swarm optimization (GSO) algorithm, this work enhances the ANFIS model. The GSO method, which models the behavior of glowworms while foraging, is employed to optimize the efficiency of the ANFIS. Additionally, to accelerate convergence during network training, the proposed method employs a hybrid learning algorithm combining the Conjugate Gradient Descent (CGD) with the Least Square Estimator (LSE). Fuzzy logic is employed in the Adaptive Backpropagation Neural Network (ABPNN) classifier for improving its performance. The results demonstrate the efficiency of the ABPNN-GSO-ANFIS algorithm in CKD diagnosis with an accuracy of 99.52%, precision of 99.34%, and recall of 97.82%. The results establish that the proposed algorithm performs better than other state-of-the-art ML algorithms.

Dhawal Powle, Department of Artificial Intelligence and Data Science, SVKM'S Narsee Monjee Institute of Management Studies Deemed to be University, Navi Mumbai, Maharashtra, India

Dhaval Powle is Final Year Student of BTECH Computer Engineering at NMIMS Deemed to be University. Throughout his academic journey, Dhaval has immersed himself in the fundamentals and advanced aspects of computer science and engineering. He has developed a solid foundation in core subjects like data structures, algorithms, computer networks, and software engineering, which has equipped him with essential problem-solving skills

Gaurav Keshari, Department of Artificial Intelligence and Data Science, SVKM'S Narsee Monjee Institute of Management Studies Deemed to be University, Navi Mumbai, Maharashtra, India.

Gaurav Keshari is a final-year B.Tech student in the Computer Engineering program at NMIMS Deemed to be University. Throughout his academic journey, Gaurav has immersed himself in the fundamentals and advanced aspects of computer science and engineering. He has developed a solid foundation in core subjects like data structures, algorithms, computer networks, and software engineering, which has equipped him with essential problem-solving skills.

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