Predicting diabetes risk using artificial intelligence models
- سال انتشار: 1404
- محل انتشار: دومین کنگره بین المللی هوش مصنوعی در علوم پزشکی
- کد COI اختصاصی: AIMS02_502
- زبان مقاله: انگلیسی
- تعداد مشاهده: 26
نویسندگان
Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
Department of Computer Engineering, Faculty of Computer and Electrical Engineering, University of Kashan, Kashan, Iran
Department of Computer Engineering, Faculty of Computer and Electrical Engineering, University of Kashan, Kashan, Iran
Clinical Research Development Unit of Kashan Shahid Beheshti hospital
چکیده
Background and Aims: Diabetes is considered as one of the most common non-communicable diseases, with the number of patients increasing each year, resulting in a variety of complications like cardiovascular, renal, and visual problems. On the other hand, artificial intelligence has recently made significant contributions to many fields, including health, helping to advance human objectives. We have also made an effort to design a method that utilize it as an aid for the early prediction of the risk of diabetes in individuals. Methods: In the present study, data from ۲۵۳,۶۸۱ registered patients from the Diabetes Health Indicators Dataset on the Kaggle website was utilized. The data included an examination of ۲۱ features including information like age, sex, blood pressure, cholesterol, smoking status, alcohol consumption status, history of stroke and other similar items. Various models were designed, and their results were compared, ultimately selecting the SVM and RANDOM FOREST models, which showed the highest accuracy of ۸۸% and ۸۷% respectively. Results: The Random Forest and SVM models demonstrated the best performance, both achieving an Accuracy of ۸۶%. The F۱-score and Recall for both models were ۸۶%, while the Precision was ۸۷% for Random Forest and ۸۸% for SVM, making them superior to other models. The Logistic Regression and Decision Tree models also showed satisfactory performance, while the KNN and XGBoost models exhibited lower predictive power. Conclusion: Considering the relatively accurate predictability of the risk of diabetes in this study, it can be seen that the use of artificial intelligence models is very helpful in predicting chronic disorders such as diabetes, and artificial intelligence with a screening approach will be very effective in health policies.کلیدواژه ها
Diabetes, Artificial Intelligence, Random Forest, SVMاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.