{"title":"应用数据挖掘技术预测临床数据库中的隐藏知识","authors":"Gunasekar Thangarasu, P. Dominic","doi":"10.1109/ICCOINS.2014.6868414","DOIUrl":null,"url":null,"abstract":"Clinical Database has enormous quantity of information about patients and their diseases. The database mainly contains clinical consultation details, family history, medical lab report and other information which are considered to taking a final diagnostic decision by physician. Clinical databases are widely utilized by the numerous researchers for predicting different diseases. The current diabetes diagnosis methods are carried out based on the impact of various medical test and the results of physical examination. The new and innovative prediction methods are projected in this research to identify the diabetic disease, its types and complications from the clinical database in an efficiently and an economically faster manner.","PeriodicalId":368100,"journal":{"name":"2014 International Conference on Computer and Information Sciences (ICCOINS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Prediction of hidden knowledge from Clinical Database using data mining techniques\",\"authors\":\"Gunasekar Thangarasu, P. Dominic\",\"doi\":\"10.1109/ICCOINS.2014.6868414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clinical Database has enormous quantity of information about patients and their diseases. The database mainly contains clinical consultation details, family history, medical lab report and other information which are considered to taking a final diagnostic decision by physician. Clinical databases are widely utilized by the numerous researchers for predicting different diseases. The current diabetes diagnosis methods are carried out based on the impact of various medical test and the results of physical examination. The new and innovative prediction methods are projected in this research to identify the diabetic disease, its types and complications from the clinical database in an efficiently and an economically faster manner.\",\"PeriodicalId\":368100,\"journal\":{\"name\":\"2014 International Conference on Computer and Information Sciences (ICCOINS)\",\"volume\":\"108 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Computer and Information Sciences (ICCOINS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCOINS.2014.6868414\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Computer and Information Sciences (ICCOINS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCOINS.2014.6868414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of hidden knowledge from Clinical Database using data mining techniques
Clinical Database has enormous quantity of information about patients and their diseases. The database mainly contains clinical consultation details, family history, medical lab report and other information which are considered to taking a final diagnostic decision by physician. Clinical databases are widely utilized by the numerous researchers for predicting different diseases. The current diabetes diagnosis methods are carried out based on the impact of various medical test and the results of physical examination. The new and innovative prediction methods are projected in this research to identify the diabetic disease, its types and complications from the clinical database in an efficiently and an economically faster manner.