Prediction of new prescription requirements for diabetes patients using big data technologies
Purpose – The study aimed to evaluate the effectiveness of using large data sets for new diabetes patient prescriptions. Design/methodology/approach – This study consisted of 101,766 individuals, who had applied to the hospital with a diabetes diagnosis and were hospitalized for 1–14 days and subjec...
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| Format: | Article |
| Language: | English |
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College of Public Health Sciences, Chulalongkorn University
2022-02-01
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| Series: | Journal of Health Research |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/JHR-05-2020-0136/full/pdf?title=prediction-of-new-prescription-requirements-for-diabetes-patients-using-big-data-technologies |
| _version_ | 1797759978961895424 |
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| author | Batuhan Bakırarar Cemil Yüksel Yasemin Yavuz |
| author_facet | Batuhan Bakırarar Cemil Yüksel Yasemin Yavuz |
| author_sort | Batuhan Bakırarar |
| collection | DOAJ |
| description | Purpose – The study aimed to evaluate the effectiveness of using large data sets for new diabetes patient prescriptions. Design/methodology/approach – This study consisted of 101,766 individuals, who had applied to the hospital with a diabetes diagnosis and were hospitalized for 1–14 days and subjected to laboratory tests and medication. Findings – With the help of Mahout and Scala, data mining methods of random forest and multilayer perceptron were used. Accuracy rates of these methods were found to be 0.879 and 0.849 for Mahout and 0.849 and 0.870 for Scala. Originality/value – The mahout random forest method provided a better prediction of new prescription requirements than the other methods according to accuracy criteria. |
| first_indexed | 2024-03-12T18:52:14Z |
| format | Article |
| id | doaj.art-1333b8419fac47c0bad4df8a24499640 |
| institution | Directory Open Access Journal |
| issn | 0857-4421 2586-940X |
| language | English |
| last_indexed | 2024-03-12T18:52:14Z |
| publishDate | 2022-02-01 |
| publisher | College of Public Health Sciences, Chulalongkorn University |
| record_format | Article |
| series | Journal of Health Research |
| spelling | doaj.art-1333b8419fac47c0bad4df8a244996402023-08-02T07:05:53ZengCollege of Public Health Sciences, Chulalongkorn UniversityJournal of Health Research0857-44212586-940X2022-02-0136233434410.1108/JHR-05-2020-0136663007Prediction of new prescription requirements for diabetes patients using big data technologiesBatuhan Bakırarar0Cemil Yüksel1Yasemin Yavuz2Biostatistics, Ankara University School of Medicine, Ankara, TurkeySurgical Oncology, University of Health Sciences, Ankara Oncology Training and Research Hospital, Ankara, TurkeyBiostatistics, Ankara University School of Medicine, Ankara, TurkeyPurpose – The study aimed to evaluate the effectiveness of using large data sets for new diabetes patient prescriptions. Design/methodology/approach – This study consisted of 101,766 individuals, who had applied to the hospital with a diabetes diagnosis and were hospitalized for 1–14 days and subjected to laboratory tests and medication. Findings – With the help of Mahout and Scala, data mining methods of random forest and multilayer perceptron were used. Accuracy rates of these methods were found to be 0.879 and 0.849 for Mahout and 0.849 and 0.870 for Scala. Originality/value – The mahout random forest method provided a better prediction of new prescription requirements than the other methods according to accuracy criteria.https://www.emerald.com/insight/content/doi/10.1108/JHR-05-2020-0136/full/pdf?title=prediction-of-new-prescription-requirements-for-diabetes-patients-using-big-data-technologiesbig dataclassificationdata miningdiabetes mellitus |
| spellingShingle | Batuhan Bakırarar Cemil Yüksel Yasemin Yavuz Prediction of new prescription requirements for diabetes patients using big data technologies Journal of Health Research big data classification data mining diabetes mellitus |
| title | Prediction of new prescription requirements for diabetes patients using big data technologies |
| title_full | Prediction of new prescription requirements for diabetes patients using big data technologies |
| title_fullStr | Prediction of new prescription requirements for diabetes patients using big data technologies |
| title_full_unstemmed | Prediction of new prescription requirements for diabetes patients using big data technologies |
| title_short | Prediction of new prescription requirements for diabetes patients using big data technologies |
| title_sort | prediction of new prescription requirements for diabetes patients using big data technologies |
| topic | big data classification data mining diabetes mellitus |
| url | https://www.emerald.com/insight/content/doi/10.1108/JHR-05-2020-0136/full/pdf?title=prediction-of-new-prescription-requirements-for-diabetes-patients-using-big-data-technologies |
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