Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/3258
Title: | Prediction of Diabetes Mellitus by using Gradient Boosting Classification | Authors: | Nusrat, Fatema Uzbaş, Betül Baykan, Ömer Kaan |
Keywords: | Diabetes Gradient Boosting Machine Learning Diyabet Gradyan Arttırma Makina Öğrenmesi |
Issue Date: | 2020 | Abstract: | Diabetes has become a pervasive and endemic health problem worldwide. It is a chronic disease and also life-threatening. It can cause health problems in many organs such as the heart, kidneys, eyes, nerves, and blood vessels. To reduce the fatality rate from diabetes, early prevention techniques are needed. Nowadays, machine learning techniques are used to predict or detect different life-threatening diseases like cancer, diabetes, heart diseases, thyroid, etc. In this study, a prediction model of diabetes mellitus was presented using the Pima Indian dataset. Three different machine learning techniques that Decision Tree (DT), Random Forest (RF) and, Gradient Boosting (GB) algorithm were used to predict diabetes mellitus and the performance analysis was performed. Confusion matrix, accuracy, F1 score, precision, recall, Cohen’s kappa were evaluated and also a ROC curve was plotted. Out of the three techniques, the best results have been achieved with GB. | URI: | https://doi.org/10.31590/ejosat.803504 https://search.trdizin.gov.tr/yayin/detay/1135912 https://hdl.handle.net/20.500.13091/3258 |
ISSN: | 2148-2683 |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections |
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File | Size | Format | |
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10.31590-ejosat.803504-1321702.pdf | 917.3 kB | Adobe PDF | View/Open |
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