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

Classification of Different Countries in Terms of Noncommunicable Diseases Using Machine Learning Techniques

34791

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.

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Noncommunicable Diseases (NCDs) , Health Care Indicators , Machine Learning

Uludağ University Journal of The Faculty of Engineering

97