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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: | Diabetes Estimation Applying AI Support Vector Machine – Algorithm Technique |
| Authors Name: | Vraj Mayur Parekh , Swati Nandusekar , Siddharth Piyush Tanna , Preksha Pranaykumar Shah |
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IJNRD_190264
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| Published Paper Id: | IJNRD2304072 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Diabetes is a persistent metabolic problem described by high blood levels. Early detection and accurate prediction of diabetes can help prevent complications and improve patient outcomes. Support Vector Machines is considered to be a prevailing machine learning algorithm technique which has been persistently used in disease prediction due to its capability to manage complex data and nonlinear relationships. In this paper, we decided to develop an SVM-based diabetes prediction system using a dataset of patient records and clinical features. A promising algorithm for diabetes prediction based on patient data and clinical features. We reviewed the most commonly used machine learning algorithms for diabetes prediction, including SVM, decision tree, logistic regression. SVM is a prevalent algorithm that has been broadly used in disease prediction studies, including diabetes prediction. Several studies have explored the use of SVM algorithm for diabetes prediction, and the results indicate that SVM achieved high accuracy, sensitivity, and specificity in predicting diabetes. The most important features in predicting diabetes include BMI, age, and fasting blood glucose level. Future studies could further explore the need and utilization of SVM with other machine learning algorithms for diabetes prediction using larger and more diverse datasets. Early detection and accurate prediction of diabetes can help prevent complications and improve patient outcomes, and machine learning algorithms, such as SVM, have the latent to be a useful tool for identifying samples at risk for developing diabetes. |
| Keywords: | Support Vector Machine, Diabetes Detection, Machine Learning |
| Cite Article: | "Diabetes Estimation Applying AI Support Vector Machine – Algorithm Technique", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.a585-a589, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304072.pdf |
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| ISSN: |
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:IJNRD2304072 Registration ID: 190264 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: a585-a589 Country: Mumbai, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304072 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304072 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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