Classification of Different Countries in Terms of Noncommunicable Diseases Using Machine Learning Techniques
<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>The aim of this study is to classify 193 countries which are members of World Health Organization (WHO) in terms of Non Communicable Diseases (N...
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| Format: | Article |
| Language: | English |
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Bursa Uludag University
2015-06-01
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| Series: | Uludağ University Journal of The Faculty of Engineering |
| Subjects: | |
| Online Access: | http://mmfdergi.uludag.edu.tr/article/view/5000087138 |
| _version_ | 1828017031811694592 |
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| author | Songül ÇINAROĞLU Keziban AVCI |
| author_facet | Songül ÇINAROĞLU Keziban AVCI |
| author_sort | Songül ÇINAROĞLU |
| collection | DOAJ |
| description | <div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>The aim of this study is to classify 193 countries which are members of World Health Organization (WHO) in terms of Non Communicable Diseases (NCDs). Support vector machine and random forest methods used for classification which are one of supervised data mining methods. An open source programme Orange used for analysis. At the end of the analysis it was seen that random forest classification performance results were better than support vector machine classification performance results. The results of this study is useful for global health care managers for fighting against Noncommunicable Diseases and producing effective policies. </span></p></div></div></div> |
| first_indexed | 2024-04-10T10:43:43Z |
| format | Article |
| id | doaj.art-d98fb8c6ff674c8ba0b8e78e847d278e |
| institution | Directory Open Access Journal |
| issn | 2148-4147 2148-4155 |
| language | English |
| last_indexed | 2024-04-10T10:43:43Z |
| publishDate | 2015-06-01 |
| publisher | Bursa Uludag University |
| record_format | Article |
| series | Uludağ University Journal of The Faculty of Engineering |
| spelling | doaj.art-d98fb8c6ff674c8ba0b8e78e847d278e2023-02-15T16:20:28ZengBursa Uludag UniversityUludağ University Journal of The Faculty of Engineering2148-41472148-41552015-06-01202899710.17482/uujfe.360995000111584Classification of Different Countries in Terms of Noncommunicable Diseases Using Machine Learning TechniquesSongül ÇINAROĞLU0Keziban AVCI1Hacettepe ÜniversitesiSağlık Bakanlığı<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>The aim of this study is to classify 193 countries which are members of World Health Organization (WHO) in terms of Non Communicable Diseases (NCDs). Support vector machine and random forest methods used for classification which are one of supervised data mining methods. An open source programme Orange used for analysis. At the end of the analysis it was seen that random forest classification performance results were better than support vector machine classification performance results. The results of this study is useful for global health care managers for fighting against Noncommunicable Diseases and producing effective policies. </span></p></div></div></div>http://mmfdergi.uludag.edu.tr/article/view/5000087138Noncommunicable Diseases (NCDs), Health Care Indicators, Machine Learning |
| spellingShingle | Songül ÇINAROĞLU Keziban AVCI Classification of Different Countries in Terms of Noncommunicable Diseases Using Machine Learning Techniques Uludağ University Journal of The Faculty of Engineering Noncommunicable Diseases (NCDs), Health Care Indicators, Machine Learning |
| title | Classification of Different Countries in Terms of Noncommunicable Diseases Using Machine Learning Techniques |
| title_full | Classification of Different Countries in Terms of Noncommunicable Diseases Using Machine Learning Techniques |
| title_fullStr | Classification of Different Countries in Terms of Noncommunicable Diseases Using Machine Learning Techniques |
| title_full_unstemmed | Classification of Different Countries in Terms of Noncommunicable Diseases Using Machine Learning Techniques |
| title_short | Classification of Different Countries in Terms of Noncommunicable Diseases Using Machine Learning Techniques |
| title_sort | classification of different countries in terms of noncommunicable diseases using machine learning techniques |
| topic | Noncommunicable Diseases (NCDs), Health Care Indicators, Machine Learning |
| url | http://mmfdergi.uludag.edu.tr/article/view/5000087138 |
| work_keys_str_mv | AT songulcinaroglu classificationofdifferentcountriesintermsofnoncommunicablediseasesusingmachinelearningtechniques AT kezibanavci classificationofdifferentcountriesintermsofnoncommunicablediseasesusingmachinelearningtechniques |