نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی کامپیوتر - دانشکده مهندسی و فناوری ـ دانشگاه مازندران ـ بابلسرـ ایران
2 گروه مهندسی کامپیوتر- دانشکده مهندسی و فناوری ـ دانشگاه مازندران ـ بابلسرـ ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Diabetes is one of the most significant factors leading to death, which can significantly result in kidney diseases, heart diseases, and sight loss. The application of data mining could be helpful for the diagnosis and treatment of this disease. Predicting diabetes is an important area of research that can help improve the treatment process. It is necessary to prevent, monitor, and raise awareness about this disease. In this article, a new method to diagnose diabetes is proposed. The proposed method includes a pre-processing stage in which outlier data is removed. Eventually, by using the K-Nearest Neighbor classifier and Extreme Gradient Boosting, samples will be classified into two classes: diabetic and non-diabetic. In the end, to improve the proposed method, a soft voting algorithm has been used to merge the two classifiers. The proposed method has been applied to the Pima diabetes dataset, which includes information on age, gender, blood pressure, glucose, and other factors related to diabetes. The proposed method in this research was evaluated using evaluation metrics such as accuracy, precision, and recall. This model achieved 91.3% accuracy, 94.5% precision, and 89.1% recall. The results indicate that the proposed model has performed better than other references by increasing the accuracy in diagnosing diabetes. Therefore, by using this model, it will be possible to identify potential diabetic patients more accurately and ultimately prevent them from becoming diabetic.
کلیدواژه‌ها [English]
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