امانی, فیروز ، محمدنیا, علیرضا ، امانی, پانیذ ، عبدالهی اصل, سهیلا ، بهادرام, محمد (1401) استفاده از روش یادگیری ماشین برای طبقه بندی BMI. Journal of Renal Endocrinology ــ 8 (e1707). ص.ص.1-5. شاپا 2423-6438
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آدرس اینترنتی رسمی : https://www.jrenendo.com/Article/jre-17072
عنوان انگليسی
Using machine learning method for classification body mass index of people for clinical decision
خلاصه انگلیسی
Introduction: Body mass index (BMI) is an acceptable method to measure overweight and obesity among the population. Objectives: The aim of this study was evaluating the application of machine learning algorithms for classifying body mass index for clinical purposes. Patients and Methods: In this descriptive study, we selected the dataset of 1316 people who selected randomly from all area of Ardabil city in Iran. Dataset included demographic and anthropometric data. Classification algorithms such as random forest (RF), Gaussian Naive Bayes (GNB), decision tree (DT), support vector machines (SVM), multi-layer perceptron (MLP), K-nearest neighbors (KNN) and logistic regression (LR) with 10-fold cross-validation were conducted to classify the data based on BMI. The performance of algorithms was evaluated with precision, recall, mean squared errors (MSE) and accuracy indices. All programing done by Python 3.7 in Jupyter Notebook. Results: According to the BMI, 603(45.8%) of all samples were normal and 713 (54.2%) were at-risk. The precision of RF, GNB, DT, SVM, MLP, KNN and LR for people at risk were 0.93, 0.86, 0.99, 0.82, 100, 0.82 and 0.99 respectively. Additionally, the accuracy of RF, GNB, DT, SVM, MLP, KNN and LR were 95%, 83%, 100%, 82%, 100%, 82% and 100 %. Conclusion: The comparison of the classifying algorithms showed that, the LR, MLP and DT had the higher accuracy than the other algorithms in detecting of people at-risk.
نوع سند : | مقاله |
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زبان سند : | انگلیسی |
نویسنده اول : | فیروز امانی |
نویسنده : | علیرضا محمدنیا |
نویسنده : | پانیذ امانی |
نویسنده : | سهیلا عبدالهی اصل |
نویسنده مسئول : | محمد بهادرام |
ضریب تاثیر و نمایه مجلات: | Indexed in: Index Copernicus |
کلیدواژه ها (انگلیسی): | Body mass index, Machine learning, Classification, Algorithm |
موضوعات : | W حرفه پزشکی > W.20.55 H9 عناوین ویژه |
بخش های دانشگاهی : | دانشكده پزشكي > گروه علوم پایه > بخش آمار حياتي |
کد شناسایی : | 16802 |
ارائه شده توسط : | دکتر فیروز امانی |
ارائه شده در تاریخ : | 10 اسفند 1401 08:43 |
آخرین تغییر : | 10 اسفند 1401 08:43 |
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