Show simple item record

dc.contributor.authorประยุทธ์ ศิลป์ชัยนามen_US
dc.date.accessioned2019-09-11T03:41:33Z
dc.date.available2019-09-11T03:41:33Z
dc.date.issued2019
dc.identifier.urihttp://repository.rmutr.ac.th/123456789/1082
dc.description.abstractThis research aims to develop and compare models for identifying chronic kidney disease patients. By using serveral Data mining techniques including K-nearest neighbor, decision tree, Random Forest, support vector machine and Naïve Bayes are used to test the classification of patients by using chronic kidney disease data from Apollo Hospital India By storing it in a relational database. Which has good efficiency, can support fast data transmission which responds to current information usage. And analyze to summarize with the image analysis system to be able to understand the information easily.en_US
dc.language.isoTHen_US
dc.publisherมหาวิทยาลัยศรีปทุมen_US
dc.subjectVisualizationen_US
dc.subjectData Mining-nearest neighboren_US
dc.subjectdecision treeen_US
dc.subjectartificial neural networken_US
dc.subjectsupport vector machineen_US
dc.subjectNaïve Bayesen_US
dc.titleData Classification Model for Chronic Kidney Disease using data mining Technique and Visualizationen_US
dc.title.alternativeการสร้างแบบจําลองจําแนกกลุ่มผู้ป่วยโรคไตเรื้อรังโดยใช้เทคนิคเหมืองข้อมูลและวิชวลไลเซชั่นen_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record