发布求助
文献互助 智能选刊 最新文献

Artificial Intelligence and Cloud Computing Conference最新文献

筛选
英文 中文
Imputation Techniques and Recursive Feature Elimination in Machine Learning Applied to Type II Diabetes Classification 机器学习中的归算技术和递归特征消除在II型糖尿病分类中的应用
Artificial Intelligence and Cloud Computing Conference Pub Date : 1900-01-01 DOI: 10.1145/3508259.3508288
V. P. Magboo, M. A. Magboo
{"title":"Imputation Techniques and Recursive Feature Elimination in Machine Learning Applied to Type II Diabetes Classification","authors":"V. P. Magboo, M. A. Magboo","doi":"10.1145/3508259.3508288","DOIUrl":"https://doi.org/10.1145/3508259.3508288","url":null,"abstract":"Type II diabetes is a chronic metabolic disease secondary to elevated blood glucose levels. Complications of this disease include heart attack, stroke, blindness, renal failure, lower limb amputation and mortality. Due to its rising prevalence and consequent mortality, it is important to identify at an early stage those patients at high risk of developing diabetes. We applied 8 machine learning techniques namely: support vector machine, logistic regression, k-nearest neighbor, naïve Bayes, decision tree, random forest, AdaBoost and XGBoost in predicting diabetes using a publicly available diabetes dataset. In our study, Naïve Bayes with median imputation and recursive feature elimination obtained the highest performance with an accuracy rate of 81.0%. Although the results are very promising, one major limitation in this study is the small number of samples in the dataset. Early accurate detection can help patients to proactively monitor their lifestyle habits mitigating the risks of complications of uncontrolled diabetes.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121181723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Using the Semantic Annotation of Web Table Data for Knowledge Base Construction 基于Web表数据语义标注的知识库构建
Artificial Intelligence and Cloud Computing Conference Pub Date : 1900-01-01 DOI: 10.1145/3508259.3508277
N. Dorodnykh, A. Shigarov, A. Y. Yurin
{"title":"Using the Semantic Annotation of Web Table Data for Knowledge Base Construction","authors":"N. Dorodnykh, A. Shigarov, A. Y. Yurin","doi":"10.1145/3508259.3508277","DOIUrl":"https://doi.org/10.1145/3508259.3508277","url":null,"abstract":"Knowledge bases are one of the main elements of intelligent systems. In that, knowledge base engineering is traditionally considered as a \"bottleneck\" in the design of such systems, and it is a deterrent factor of their widespread use. Computer-aided construction of knowledge base that employs various information sources is a promising area of scientific research. Web tables may be one of such sources, and they are among the most accessible and common ways for representing and storing tabular information. In this paper, we propose to automate ontological knowledge base engineering by using the semantic annotation of data from web tables and present an approach to filling the ontological knowledge base with specific entities (facts) extracted from web tables. This approach is implemented in the form of a tool. The tool has been used to solve the problem of forming domain knowledge graphs for the TALISMAN framework.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130895444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
首页 上一页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信