Weighted fuzzy C means and enhanced adaptive neuro-fuzzy inference based chronic kidney disease classification
Chronic nephritic sickness is another name for Chronic Kidney Disease (CKD). Numerous complications, such as elevated blood levels, anemia, weak bones, and nerve damage, constitute a problem. It is usually possible to prevent chronic uropathy from getting worse by early identification and treatment....
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Ayandegan Institute of Higher Education,
2024-03-01
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| Series: | Journal of Fuzzy Extension and Applications |
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| Online Access: | https://www.journal-fea.com/article_194735_9d221219dfc663a41dc344204ea451f2.pdf |
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| author | Maria Lincy Jacquline Natarajan Sudha |
| author_facet | Maria Lincy Jacquline Natarajan Sudha |
| author_sort | Maria Lincy Jacquline |
| collection | DOAJ |
| description | Chronic nephritic sickness is another name for Chronic Kidney Disease (CKD). Numerous complications, such as elevated blood levels, anemia, weak bones, and nerve damage, constitute a problem. It is usually possible to prevent chronic uropathy from getting worse by early identification and treatment. To circumvent these problems, current research has presented the Fruit Fly Optimization Algorithm (FFOA) and effective Multi-Kernel Support Vector Machine (MKSVM) for illness classification. Finding the best features from a collection is usually done using FFOA. MKSVM categorizes medical data using chosen dataset criteria. The accuracy of the classifier will be impacted by any range of variations in data obtained for this study. MKSVM continues to yield more incorrectly classified findings. To resolve those problems, a preprocessing step based on min-max normalization is used to normalize the input CKD data values scale. Then, significant features will be selected using Improved FFOA (IFFOA). The selected features will be clustered using Weighted Fuzzy C Means clustering (WFCM) to predict the class label of the data sample and reduce the misclassification results. Finally, as normal or abnormal, CKD classification will be performed using the Enhanced Adaptive Neuro Fuzzy Inference System (EANFIS). The suggested strategy efficacy is demonstrated by findings in fields of recall, accuracy, precision, and f-measure. |
| format | Article |
| id | doaj-art-a61500b709d747a1b0718234ce020007 |
| institution | Kabale University |
| issn | 2783-1442 2717-3453 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | Journal of Fuzzy Extension and Applications |
| spelling | doaj-art-a61500b709d747a1b0718234ce0200072025-01-30T15:07:00ZengAyandegan Institute of Higher Education,Journal of Fuzzy Extension and Applications2783-14422717-34532024-03-015110011510.22105/jfea.2024.437690.1376194735Weighted fuzzy C means and enhanced adaptive neuro-fuzzy inference based chronic kidney disease classificationMaria Lincy Jacquline0Natarajan Sudha1Department of Computer Science, Bishop Appasamy College of Arts and Science, Coimbatore, TamiNadu, India.Department of Computer Science, Bishop Appasamy College of Arts and Science, Coimbatore, India.Chronic nephritic sickness is another name for Chronic Kidney Disease (CKD). Numerous complications, such as elevated blood levels, anemia, weak bones, and nerve damage, constitute a problem. It is usually possible to prevent chronic uropathy from getting worse by early identification and treatment. To circumvent these problems, current research has presented the Fruit Fly Optimization Algorithm (FFOA) and effective Multi-Kernel Support Vector Machine (MKSVM) for illness classification. Finding the best features from a collection is usually done using FFOA. MKSVM categorizes medical data using chosen dataset criteria. The accuracy of the classifier will be impacted by any range of variations in data obtained for this study. MKSVM continues to yield more incorrectly classified findings. To resolve those problems, a preprocessing step based on min-max normalization is used to normalize the input CKD data values scale. Then, significant features will be selected using Improved FFOA (IFFOA). The selected features will be clustered using Weighted Fuzzy C Means clustering (WFCM) to predict the class label of the data sample and reduce the misclassification results. Finally, as normal or abnormal, CKD classification will be performed using the Enhanced Adaptive Neuro Fuzzy Inference System (EANFIS). The suggested strategy efficacy is demonstrated by findings in fields of recall, accuracy, precision, and f-measure.https://www.journal-fea.com/article_194735_9d221219dfc663a41dc344204ea451f2.pdfmulti-kernel support vector machinefruit fly optimization algorithmchronic kidney diseasesignificant featuresweighted fuzzy c meansadaptive neuro-fuzzy inference system |
| spellingShingle | Maria Lincy Jacquline Natarajan Sudha Weighted fuzzy C means and enhanced adaptive neuro-fuzzy inference based chronic kidney disease classification Journal of Fuzzy Extension and Applications multi-kernel support vector machine fruit fly optimization algorithm chronic kidney disease significant features weighted fuzzy c means adaptive neuro-fuzzy inference system |
| title | Weighted fuzzy C means and enhanced adaptive neuro-fuzzy inference based chronic kidney disease classification |
| title_full | Weighted fuzzy C means and enhanced adaptive neuro-fuzzy inference based chronic kidney disease classification |
| title_fullStr | Weighted fuzzy C means and enhanced adaptive neuro-fuzzy inference based chronic kidney disease classification |
| title_full_unstemmed | Weighted fuzzy C means and enhanced adaptive neuro-fuzzy inference based chronic kidney disease classification |
| title_short | Weighted fuzzy C means and enhanced adaptive neuro-fuzzy inference based chronic kidney disease classification |
| title_sort | weighted fuzzy c means and enhanced adaptive neuro fuzzy inference based chronic kidney disease classification |
| topic | multi-kernel support vector machine fruit fly optimization algorithm chronic kidney disease significant features weighted fuzzy c means adaptive neuro-fuzzy inference system |
| url | https://www.journal-fea.com/article_194735_9d221219dfc663a41dc344204ea451f2.pdf |
| work_keys_str_mv | AT marialincyjacquline weightedfuzzycmeansandenhancedadaptiveneurofuzzyinferencebasedchronickidneydiseaseclassification AT natarajansudha weightedfuzzycmeansandenhancedadaptiveneurofuzzyinferencebasedchronickidneydiseaseclassification |