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RISS 인기검색어
- 검색키워드
- 저자 : ( G. Keerthi ) (검색결과 2 건)
- 무료
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( G. Keerthi ),( M. S. Abirami ) 한국감성과학회 2021 한국감성과학회 국제학술대회(ICES) Vol.2021 No.-
Diabetes is a leading reason of death, disability, and economic loss around the world. Type 2 diabetes is the maximum shared kind of diabetes in women (80-90 percent worldwide).It can be avoided or postponed by receiving the appropriate maintenance and interventions, including an initial diagnosis. There has remained a lot of progress in the area of medical diagnosis using many machine learning algorithms. However, due to incomplete medical data sets, accuracy suffers, resulting in a higher frequency of misclassifications, which might lead to dangerous complications. Many researchers find that accurately predicting and diagnosing a disease is a difficult scientific topic. As a result, the goal was to improve the diagnostic. The first technique is to collect the dataset, which comprises of 769 pregnant women's records. On the foundation of accuracy, machine learning approaches are utilized to forecast diabetes and non-diabetes women. We used seven machine learning algorithms to calculate diabetes using the dataset. We discovered that a diabetes prediction model that combines Linear Regression and Support Vector Machine performs well, with an accuracy of 77 percent -78 percent.
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( G. Keerthi ),( Dr. M. S. Abirami ) 한국감성과학회 2021 추계학술대회 Vol.2021 No.0
Diabetes is a leading reason of death, disability, and economic loss around the world. Type 2 diabetes is the maximum shared kind of diabetes in women (80-90 percent worldwide).It can be avoided or postponed by receiving the appropriate maintenance and interventions, including an initial diagnosis. There has remained a lot of progress in the area of medical diagnosis using many machine learning algorithms. However, due to incomplete medical data sets, accuracy suffers, resulting in a higher frequency of misclassifications, which might lead to dangerous complications. Many researchers find that accurately predicting and diagnosing a disease is a difficult scientific topic. As a result, the goal was to improve the diagnostic. The first technique is to collect the dataset, which comprises of 769 pregnant women's records. On the foundation of accuracy, machine learning approaches are utilized to forecast diabetes and non-diabetes women. We used seven machine learning algorithms to calculate diabetes using the dataset. We discovered that a diabetes prediction model that combines Linear Regression and Support Vector Machine performs well, with an accuracy of 77 percent -78 percent.
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