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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | LIVER DISORDER DETECTION USING FUZZY SUPPORT VECTOR MACHINE |
| Authors Name: | B S M MANASA , B HEMA SUSHMA SRI , V MOHAN SAI , R SATYA SAI , Dr Barenya B. Hazarika,Khoiram Motilal Singh |
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IJNRD_191259
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| Published Paper Id: | IJNRD2304290 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | The total number of samples in each class varies in real-world binary classification situations. Alcoholism or the effects of viruses have a genetic issue with how the human liver functions. If not found in the early stages, it might cause cancer or liver failure. The suggested method aims to use liver function to identify early liver disorders. The proposed DWTWSVM and DWLSTSVM are compared to support vector machines (SVM), twin SVMs (TWSVM), least squares TWSVMs (LSTWSVM), fuzzy TWSVMs (FTWSVM), improved fuzzy least squares TWSVMs (IFLSTWSVM), and SVM for binary CIL in terms of model performance in terms of F1- score, G-mean, recall, and precision. In order to confirm the viability and application of the proposed models, a statistical research based on F1-score and G-mean on RW datasets is done. In a number of pertinent unbalanced artificial and real-world datasets, the outcomes are quantified using the geometric mean and area under the curve (G- mean) . The outcomes are contrasted with SVM, enhanced fuzzy least squares SVM, fuzzy least squares SVM with randomness, fuzzy least squares SVM with affinity and class probabilities, and fuzzy least squares SVM. The research primarily focuses on the use of fuzzy twin SVMs to the diagnosis of liver disease. In order to confirm the value and applicability of the proposed models, a statistical research based on F1-score and G-mean on RW datasets is carried out. |
| Keywords: | Fuzzy Svm , Liver disease, Twin Support Vector Machine, G - Mean. |
| Cite Article: | "LIVER DISORDER DETECTION USING FUZZY SUPPORT VECTOR MACHINE", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.c715-c718, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304290.pdf |
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2304290 Registration ID: 191259 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: c715-c718 Country: -, -, - Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304290 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304290 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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