Simple Prediction of Type 2 Diabetes Mellitus via Decision Tree Modeling

Mehrab Sayadi; Mohammad Javad  Zibaeenezhad; Seyyed Mohammad Taghi  Ayatollahi

International Cardiovascular Research Journal

The Official Annually Journal of Cardiovascular Research Center, Shiraz University of Medical Sciences

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Simple Prediction of Type 2 Diabetes Mellitus via Decision Tree Modeling

Author(s):

Mehrab Sayadi¹,

Mohammad Javad Zibaeenezhad²,

Seyyed Mohammad Taghi Ayatollahi^1,*

1Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

2Cardiovascular Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

International Cardiovascular Research Journal:Vol. 11, issue 2; e10657

Published online:Jun 30, 2017

Article type:Research Article

Received:May 31, 2017

Accepted:Jan 23, 2017

How to Cite:Sayadi M, Zibaeenezhad MJ, Ayatollahi SMT. Simple Prediction of Type 2 Diabetes Mellitus via Decision Tree Modeling. Int Cardiovasc Res J. 2017;11(2):e10657. doi:

Abstract

Background: Type 2 Diabetes Mellitus (T2DM) is one of the most important risk factors in cardiovascular disorders considered as a common clinical and public health problem. Early diagnosis can reduce the burden of the disease. Decision tree, as an advanced data mining method, can be used as a reliable tool to predict T2DM.

Objectives: This study aimed to present a simple model for predicting T2DM using decision tree modeling.

Materials and Methods: This analytical model-based study used a part of the cohort data obtained from a database in Healthy Heart House of Shiraz, Iran. The data included routine information, such as age, gender, Body Mass Index (BMI), family history of diabetes, and systolic and diastolic blood pressure, which were obtained from the individuals referred for gathering baseline data in Shiraz cohort study from 2014 to 2015. Diabetes diagnosis was used as binary datum. Decision tree technique and J48 algorithm were applied using the WEKA software (version 3.7.5, New Zealand). Additionally, Receiver Operator Characteristic (ROC) curve and Area Under Curve (AUC) were used for checking the goodness of fit.

Results: The age of the 11302 cases obtained after data preparation ranged from 18 to 89 years with the mean age of 48.1 ± 11.4 years. Additionally, 51.1% of the cases were male. In the tree structure, blood pressure and age were placed where most information was gained. In our model, however, gender was not important and was placed on the final branch of the tree. Total precision and AUC were 87% and 89%, respectively. This indicated that the model had good accuracy for distinguishing patients from normal individuals.

Conclusions: The results showed that T2DM could be predicted via decision tree model without laboratory tests. Thus, this model can be used in pre-clinical and public health screening programs.

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The references are available in PDF.

Early Diagnosis of Diabetes Mellitus Using Data Mining and Classification Techniques

Seyed Ataaldin Mahmoudinejad Dezfuli,

Seyedeh Razieh Mahmoudinejad Dezfuli,

Seyed Vafaaldin Mahmoudinejad Dezfuli,

Younes Kiani

Mahmoudinejad Dezfuli SA, Mahmoudinejad Dezfuli SR, Mahmoudinejad Dezfuli SV, Kiani Y. Early Diagnosis of Diabetes Mellitus Using Data Mining and Classification Techniques. Jundishapur J Chronic Dis Care. 2019;8(3):e94173. doi: https://doi.org/10.5812/jjcdc.94173

30

Apr

2015

An Application of Association Rule Mining to Extract Risk Pattern for Type 2 Diabetes Using Tehran Lipid and Glucose Study Database

Azra Ramezankhani,

Omid Pournik,

Jamal Shahrabi,

Fereidoun Azizi,

Farzad Hadaegh

Ramezankhani A, Pournik O, Shahrabi J, Azizi F, Hadaegh F. An Application of Association Rule Mining to Extract Risk Pattern for Type 2 Diabetes Using Tehran Lipid and Glucose Study Database. Int J Endocrinol Metab. 2015;13(2):e25389. doi: https://doi.org/10.5812/ijem.25389

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A Hybrid Deep Learning and Explainable AI Framework for Early Detection of Type 2 Diabetes: A Multi-Factor Approach

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Kashani M, Barzehkar S, Kamani M. A Hybrid Deep Learning and Explainable AI Framework for Early Detection of Type 2 Diabetes: A Multi-Factor Approach. Shiraz E-Med J. 2026;27(2):e166066. doi: https://doi.org/10.5812/semj-166066

12

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2022

A Comparison of Three Research Methods: Logistic Regression, Decision Tree, and Random Forest to Reveal Association of Type 2 Diabetes with Risk Factors and Classify Subjects in a Military Population

Mohammad Sahebhonar,

Mehrzad Gholampour Dehaki,

Mohammad Hassan Kazemi-Galougahi,

Saeed Soleiman-Meigooni

Sahebhonar M, Gholampour Dehaki M, Kazemi-Galougahi MH, Soleiman-Meigooni S. A Comparison of Three Research Methods: Logistic Regression, Decision Tree, and Random Forest to Reveal Association of Type 2 Diabetes with Risk Factors and Classify Subjects in a Military Population. J Arch Mil Med. 2022;10(2):e118525. doi: https://doi.org/10.5812/jamm-118525

8

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Factors associated with incidence of type II diabetes in pre-diabetic women using Bayesian Model Averaging

maryam mahdavi,

Mehrabi Yadollah,

davood khalili,

Ahmad Reza Baghestani,

farideh bagherzadeh khiabani,

samaneh mansoori

mahdavi M, Yadollah M, khalili D, Baghestani AR, bagherzadeh khiabani F, et al. Factors associated with incidence of type II diabetes in pre-diabetic women using Bayesian Model Averaging. koomesh. 2017;19(3):e152905. doi:

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Author(s):

Mehrab Sayadi:[PubMed][Scholar]
Mohammad Javad Zibaeenezhad:[PubMed][Scholar]
Seyyed Mohammad Taghi Ayatollahi:[PubMed][Scholar]

The Official Annually Journal of Cardiovascular Research Center, Shiraz University of Medical Sciences

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Simple Prediction of Type 2 Diabetes Mellitus via Decision Tree Modeling

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