Please use this identifier to cite or link to this item: https://publication.npru.ac.th/jspui/handle/123456789/1931
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dc.contributor.authorNamkaew, Aphisit-
dc.contributor.authorHengpraprohm, Kairung-
dc.contributor.authorHengpraprohm, Supojn-
dc.date.accessioned2023-11-06T09:27:54Z-
dc.date.available2023-11-06T09:27:54Z-
dc.date.issued2023-07-14-
dc.identifier.uri978-974-7063-43-1-
dc.identifier.urihttps://publication.npru.ac.th/jspui/handle/123456789/1931-
dc.description.abstractThe objectives of this research are 1) to compare the efficiency of lung cancer data classification using four data mining techniques: Decision Tree, Naive Bayes, Support Vector Machine, and Artificial Neural Network; 2) to improve the data classification model for lung cancer datasets which consisted of 16 columns and 310 rows. The performance was tested by the 10-Fold Cross Validation method using Rapid Miner Studio 9.10 software. The results show that the Artificial Neural Network gave the best efficiency with an accuracy of 89.25 %, recall of 90.12 %, and precision of 97.33 %. After that, the efficiency of data classification of the Artificial Neural Network is improved by using the majority vote ensemble method. It improved the efficiency with a classification accuracy of 91.40 % and a recall of 92.59 % and a precision is 97.40 %, respectively.en_US
dc.publisherThe 15th NPRU National Academic Conference Nakhon Pathom Rajabhat Universityen_US
dc.relation.ispartofseriesProceedings of the 15th NPRU National Academic Conference;557-
dc.subjectdata miningen_US
dc.subjectdecision treeen_US
dc.subjectnaive baysen_US
dc.subjectsupport vector machineen_US
dc.subjectdata classificationen_US
dc.titleImproving the efficiency of lung cancer data classification using ensemble techniqueen_US
dc.title.alternativeการเพิ่มประสิทธิภาพในการจาแนกข้อมูลโรคมะเร็งปอดด้วยวิธีการกลุ่มก้อนข้อมูลen_US
dc.typeArticleen_US
Appears in Collections:Proceedings of the 15th NPRU National Academic Conference

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