{"title":"A proposed framework using CAC algorithm to predict systemic lupus erythematosus (SLE)","authors":"S. Gomathi, V. Narayani","doi":"10.1109/STARTUP.2016.7583974","DOIUrl":null,"url":null,"abstract":"The paper proposes new framework to predict the chronic Lupus disease. The new algorithm has been proposed which is best suitable for supervised, semi supervised and unsupervised data. The algorithm is named as CAC (Clustering Association and Classification). The best algorithms are selected based on the accuracy. The 8 major attributes to diagnose lupus has been identified and considered for prediction purpose. The new framework is designed with four phases. The analysis of 27 lupus patients has been tabulated. A new tool is being developed to predict using CAC algorithm.","PeriodicalId":355852,"journal":{"name":"2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STARTUP.2016.7583974","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The paper proposes new framework to predict the chronic Lupus disease. The new algorithm has been proposed which is best suitable for supervised, semi supervised and unsupervised data. The algorithm is named as CAC (Clustering Association and Classification). The best algorithms are selected based on the accuracy. The 8 major attributes to diagnose lupus has been identified and considered for prediction purpose. The new framework is designed with four phases. The analysis of 27 lupus patients has been tabulated. A new tool is being developed to predict using CAC algorithm.
本文提出了慢性狼疮疾病预测的新框架。提出了一种适用于有监督、半监督和无监督数据的新算法。该算法被命名为CAC (Clustering Association and Classification)。根据准确率选择最佳算法。诊断狼疮的8个主要属性已被确定并考虑用于预测目的。新框架的设计分为四个阶段。将27例狼疮患者的分析结果制成表格。目前正在开发一种利用CAC算法进行预测的新工具。
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