Author/Authors
ÇINAROĞLU, Songül Hacettepe Üniversitesi, Beytepe Yerleşkesi - İktisadi ve İdari Bil Fakültesi - Sağlık İdaresi Bölümü, Turkey , AVCI, Keziban Türkiye Yüksek İhtisas Eğitim ve Araştırma Hastanesi, Turkey
Title Of Article
Classification of Different Countries in Terms of Noncommunicable Diseases Using Machine Learning Techniques
شماره ركورد
34791
Abstract
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.
From Page
89
NaturalLanguageKeyword
Noncommunicable Diseases (NCDs) , Health Care Indicators , Machine Learning
JournalTitle
Uludağ University Journal of The Faculty of Engineering
To Page
97
JournalTitle
Uludağ University Journal of The Faculty of Engineering
Link To Document