Publication: A study on using Python vs Weka on dialysis data analysis
dc.contributor.author | Jarernsri Mitrpanont | en_US |
dc.contributor.author | Wudhichart Sawangphol | en_US |
dc.contributor.author | Thanita Vithantirawat | en_US |
dc.contributor.author | Sinattaya Paengkaew | en_US |
dc.contributor.author | Prameyuda Suwannasing | en_US |
dc.contributor.author | Atthapan Daramas | en_US |
dc.contributor.author | Yi Cheng Chen | en_US |
dc.contributor.other | National Central University Taiwan | en_US |
dc.contributor.other | Mahidol University | en_US |
dc.date.accessioned | 2019-08-23T10:57:58Z | |
dc.date.available | 2019-08-23T10:57:58Z | |
dc.date.issued | 2018-01-12 | en_US |
dc.description.abstract | © 2017 IEEE. Health data has been drastically increasing in capacity and variety. Due to large and complex collection of datasets, it is difficult to process data using traditional data processing techniques. Machine Learning techniques, such as KNN and Naïve Bayes, have been used. Python and Weka are tools that are widely used in the field of data analytics. Therefore, this paper gives the comprehensive comparison between both tools together with some machine learning algorithms on data analytic of Dialysis Dataset. The results show that using Python provides the better performance in term of correct/incorrect instances, precision, and recall. | en_US |
dc.identifier.citation | Proceeding of 2017 2nd International Conference on Information Technology, INCIT 2017. Vol.2018-January, (2018), 1-6 | en_US |
dc.identifier.doi | 10.1109/INCIT.2017.8257883 | en_US |
dc.identifier.other | 2-s2.0-85049463756 | en_US |
dc.identifier.uri | https://repository.li.mahidol.ac.th/handle/20.500.14594/45655 | |
dc.rights | Mahidol University | en_US |
dc.rights.holder | SCOPUS | en_US |
dc.source.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049463756&origin=inward | en_US |
dc.subject | Computer Science | en_US |
dc.title | A study on using Python vs Weka on dialysis data analysis | en_US |
dc.type | Conference Paper | en_US |
dspace.entity.type | Publication | |
mu.datasource.scopus | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049463756&origin=inward | en_US |