Jorjani Biomedicine Journal
فصلنامه علمی پژوهشی زیست پزشکی جرجانی
Jorjani Biomed J
Medical Sciences
http://goums.ac.ir/jorjanijournal
1
admin
2645-3509
10.52547/jorjanibiomedj
en
jalali
1399
7
1
gregorian
2020
10
1
8
3
online
1
fulltext
en
Type 2 Diabetes Prediction Using Machine Learning Algorithms
آمار زیستی
Bio-statistics
تحقیقی
Original article
<div style="text-align: justify;"><strong><em>Background and Objectives</em></strong>: 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.<br>
<strong><em>Material and Methods</em></strong>: 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.<br>
<strong><em>Results</em></strong><strong>: </strong>The model based on the gradient boosting algorithm showed the best performance with a prediction accuracy of %95.50.<br>
<strong><em>Conclusion</em></strong><strong>: </strong>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.</div>
Prediction,diabetes,machine,learning,gradient boosting,ROC curve,
4
18
http://goums.ac.ir/jorjanijournal/browse.php?a_code=A-10-745-1&slc_lang=en&sid=1
parisa
Karimi Darabi
p_karimi@email.kntu.ac.ir
10031947532846006217
0000000347640403
Yes
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
Mohammad Jafar
Tarokh
mjtarokh@kntu.ac.ir
10031947532846006218
10031947532846006218
No
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran