Diabetes mellitus data classification by cascading of feature selection methods and ensemble learning algorithms
Автор: Kemal Akyol, Baha Şen
Журнал: International Journal of Modern Education and Computer Science @ijmecs
Статья в выпуске: 6 vol.10, 2018 года.
Бесплатный доступ
Diabetes is a chronic disease related to the rise of levels of blood glucose. The disease that leads to serious damage to the heart, blood vessels, eyes, kidneys, and nerves is one of the reasons of death among the people in the world. There are two main types of diabetes: Type 1 and Type 2. The former is a chronic condition in which the pancreas produces little or no insulin by itself. The latter usually in adults, occurs when insulin level is insufficient. Classification of diabetes mellitus data which is one of the reasons of death among the people in the world is important. This study which successfully distinguishes diabetes or normal persons contains two major steps. In the first step, the feature selection or weighting methods are analyzed to find the most effective attributes for this disease. In the further step, the performances of AdaBoost, Gradient Boosted Trees and Random Forest ensemble learning algorithms are evaluated. According to experimental results, the prediction accuracy of the combination of Stability Selection method and AdaBoost learning algorithm is a little better than other algorithms with the classification accuracy by 73.88%.
Diabetes mellitus, feature selection, ensemble learning, AdaBoost, Gradient Boosted Trees, Random Forest
Короткий адрес: https://sciup.org/15016768
IDR: 15016768 | DOI: 10.5815/ijmecs.2018.06.02
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