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Paper
Title

Type 2 Diabetes Prediction Using Machine Learning Algorithms

فصلنامه زیست پزشکی جرجانی، دوره: 8، شماره: 3
Year: 1399
COI: JR_JOBJ-8-3_002
Language: EnglishView: 163
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Authors

parisa Karimi Darabi - Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
Mohammad Jafar Tarokh - Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

Abstract:

Background and Objectives: Currently, diabetes is one of the leading causes of death in the world. According to several factors diagnosis of this disease is complex and prone to human error. This study aimed to analyze the risk of having diabetes based on laboratory information, life style and, family history with the help of machine learning algorithms. When the model is trained properly, people can examine their risk of having diabetes. Material and Methods: To classify patients, by using Python, eight different machine learning algorithms (Logistic Regression, Nearest Neighbor, Decision Tree, Random Forest, Support Vector Machine, Naive Bayesian, Neural Network and Gradient Boosting) were analysed. were evaluated by accuracy, sensitivity, specificity and ROC curve parameters. Results: The model based on the gradient boosting algorithm showed the best performance with a prediction accuracy of %95.50. Conclusion: In the future, this model can be used for diagnosis diabete. The basis of this study is to do more research and develop models such as other learning machine algorithms.

Keywords:

Paper COI Code

This Paper COI Code is JR_JOBJ-8-3_002. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/1153590/

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Karimi Darabi, parisa and Tarokh, Mohammad Jafar,1399,Type 2 Diabetes Prediction Using Machine Learning Algorithms,https://civilica.com/doc/1153590

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The specifications of the publisher center of this Paper are as follows:
Type of center: دانشگاه دولتی
Paper count: 11,235
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