Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease
A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substa...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671124000457 |
| _version_ | 1797256291558621184 |
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| author | Vineetha KR M.S. Maharajan Bhagyashree K N. Sivakumar |
| author_facet | Vineetha KR M.S. Maharajan Bhagyashree K N. Sivakumar |
| author_sort | Vineetha KR |
| collection | DOAJ |
| description | A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98 % accurate in terms of efficiency. |
| first_indexed | 2024-03-07T19:40:50Z |
| format | Article |
| id | doaj.art-a15efbc0648144d9b8109617fd3a4d97 |
| institution | Directory Open Access Journal |
| issn | 2772-6711 |
| language | English |
| last_indexed | 2024-04-24T22:19:25Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj.art-a15efbc0648144d9b8109617fd3a4d972024-03-20T06:11:58ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112024-03-017100463Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney diseaseVineetha KR0M.S. Maharajan1Bhagyashree K2N. Sivakumar3Computer Science, Christ Deemed to be University, Bangalore, Karnataka-560029, India; Corresponding author.Department of ECE, SRM Institute of Science and Technology, Kattankulathur, Chennai, IndiaElectronics and Communication Engineering, Alva's Institute of Engineering and Technology, Mijar, Moodbidri, Karnataka 74225, IndiaArtificial Intelligence & Data Science, Panimalar Engineering College, Chennai, Tamilnadu 600123, IndiaA steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98 % accurate in terms of efficiency.http://www.sciencedirect.com/science/article/pii/S2772671124000457(ABPNN-ANFIS)DL algorithmsUCI CKD DatasetMATLABChronic kidney disease (CKD) |
| spellingShingle | Vineetha KR M.S. Maharajan Bhagyashree K N. Sivakumar Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease e-Prime: Advances in Electrical Engineering, Electronics and Energy (ABPNN-ANFIS) DL algorithms UCI CKD Dataset MATLAB Chronic kidney disease (CKD) |
| title | Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease |
| title_full | Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease |
| title_fullStr | Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease |
| title_full_unstemmed | Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease |
| title_short | Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease |
| title_sort | classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease |
| topic | (ABPNN-ANFIS) DL algorithms UCI CKD Dataset MATLAB Chronic kidney disease (CKD) |
| url | http://www.sciencedirect.com/science/article/pii/S2772671124000457 |
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