Volume 8, Issue 3 (10-2020)
Jorjani Biomed J 2020, 8(3): 4-18 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Karimi Darabi P, Tarokh M J. Type 2 Diabetes Prediction Using Machine Learning Algorithms. Jorjani Biomed J 2020; 8 (3) :4-18
URL: http://goums.ac.ir/jorjanijournal/article-1-738-en.html
URL: http://goums.ac.ir/jorjanijournal/article-1-738-en.html
1- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran , p_karimi@email.kntu.ac.ir
2- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
2- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
Abstract: (5865 Views)
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.
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.
Type of Article: Original article |
Subject:
Bio-statistics
Received: 2020/07/15 | Accepted: 2020/08/5 | Published: 2020/10/1
Received: 2020/07/15 | Accepted: 2020/08/5 | Published: 2020/10/1
References
1. Carter, Jake A., et al. "Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes." Expert Systems with Applications 115 (2019): 245-255. [view at publisher] [DOI] [Google Scholar]
2. Ogurtsova, Katherine, et al. "IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040." Diabetes research and clinical practice 128 (2017): 40-50. [view at publisher] [DOI] [Google Scholar]
3. Qin, Hailun, et al. "Triglyceride to high‐density lipoprotein cholesterol ratio is associated with incident diabetes in men: A retrospective study of Chinese individuals." Journal of Diabetes Investigation 11.1 (2020): 192-198. [view at publisher] [DOI] [Google Scholar]
4. Mujumdar, Aishwarya, and V. Vaidehi. "Diabetes prediction using machine learning algorithms." Procedia Computer Science 165 (2019): 292-299. [view at publisher] [DOI] [Google Scholar]
5. Zhang, Liying, et al. "Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: The Henan Rural Cohort Study." Scientific reports 10.1 (2020): 1-10. [DOI] [Google Scholar]
6. Viloria, Amelec, et al. "Diabetes Diagnostic Prediction Using Vector Support Machines." Procedia Computer Science 170 (2020): 376-381. [view at publisher] [DOI] [Google Scholar]
7. Dinh, A., Miertschin, S., Young, A. & Mohanty, S. D. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Med. Inform. Decis. Mak. 19, 211 (2019). [view at publisher] [DOI] [Google Scholar]
8. Ramezankhani, A. et al. Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study. Diabetes research and clinical practice 105, 391-398 (2014). [view at publisher] [DOI] [Google Scholar]
9. Ellahham, Samer. "Artificial Intelligence in Diabetes Care." The American Journal of Medicine (2020). [view at publisher] [DOI] [Google Scholar]
10. Kavakiotis, Ioannis, et al. "Machine learning and data mining methods in diabetes research." Computational and structural biotechnology journal 15 (2017): 104-116. [view at publisher] [DOI] [Google Scholar]
11. Gao, Feng, et al. "Independent effect of alanine transaminase on the incidence of type 2 diabetes mellitus, stratified by age and gender: A secondary analysis based on a large cohort study in China." Clinica Chimica Acta 495 (2019): 54-59. [view at publisher] [DOI] [Google Scholar]
12. Chen, Zhuangsen, et al. "Association of Triglyceride to high-density lipoprotein cholesterol ratio and incident of diabetes mellitus: a secondary retrospective analysis based on a Chinese cohort study." Lipids in health and disease 19.1 (2020): 1-11. [view at publisher] [DOI] [Google Scholar]
13. Chen, Ying, et al. "Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study." BMJ open 8.9 (2018): e021768. [view at publisher] [DOI] [Google Scholar]
14. Lin, Zeyin, et al. "A nomogram for predicting 5-year incidence of type 2 diabetes in a Chinese population." Endocrine 67.3 (2020): 561-568. [view at publisher] [DOI] [Google Scholar]
15. Patil, Ratna, and Sharavari Tamane. "A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Diabetes." International Journal of Electrical & Computer Engineering (2088-8708) 8 (2018). [view at publisher] [DOI] [Google Scholar]
16. Karun, Shweta, Aishwarya Raj, and Girija Attigeri. "Comparative Analysis of Prediction Algorithms for Diabetes." Advances in Computer Communication and Computational Sciences. Springer, Singapore, 2019. 177-187. [view at publisher] [DOI] [Google Scholar]
17. Choudhury, Ambika, and Deepak Gupta. "A survey on medical diagnosis of diabetes using machine learning techniques." Recent Developments in Machine Learning and Data Analytics. Springer, Singapore, 2019. 67-78. [view at publisher] [DOI] [Google Scholar]
18. Kaur, Harleen, and Vinita Kumari. "Predictive modelling and analytics for diabetes using a machine learning approach." Applied computing and informatics (2018). [view at publisher] [Google Scholar]
19. Devi, R. Delshi Howsalya, Anita Bai, and N. Nagarajan. "A novel hybrid approach for diagnosing diabetes mellitus using farthest first and support vector machine algorithms." Obesity Medicine 17 (2020): 100152. [view at publisher] [DOI] [Google Scholar]
20. Kumar, Prince, Shruti Garg, and Ashwani Garg. "Assessment of Anxiety, Depression and Stress using Machine Learning Models." Procedia Computer Science 171 (2020): 1989-1998. [view at publisher] [DOI] [Google Scholar]
21. Tigga, Neha Prerna, and Shruti Garg. "Prediction of Type 2 Diabetes using Machine Learning Classification Methods." Procedia Computer Science 167 (2020): 706-716. [view at publisher] [DOI] [Google Scholar]
22. Noble, William S. "What is a support vector machine?." Nature biotechnology 24.12 (2006): 1565-1567. [DOI]
23. Karabatak, Murat. "A new classifier for breast cancer detection based on Naïve Bayesian." Measurement 72 (2015): 32-36. [view at publisher] [DOI] [Google Scholar]
24. text reviews classification." Baltic Journal of Modern Computing 5.2 (2017): 221.
25. Gardner, Matt W., and S. R. Dorling. "Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences." Atmospheric environment 32.14-15 (1998): 2627-2636. [view at publisher] [DOI] [Google Scholar]
26. E Al Daoud, "Intrusion Detection Using a New Particle Swarm Method and Support Vector Machines," World Academy of Science, Engineering and Technology, vol. 77, 59-62, 2013. [Google Scholar]
27. E. Al Daoud, H Turabieh, "New empirical nonparametric kernels for support vector machine classification," Applied Soft Computing, vol. 13, no. 4, 1759-1765, 2013. [view at publisher] [DOI] [Google Scholar]
28. E. Al Daoud, "An Efficient Algorithm for Finding a Fuzzy Rough Set Reduct Using an Improved Harmony Search," I.J. Modern Education and Computer Science, vol. 7, no. 2, pp16-23, 2015. [DOI] [Google Scholar]
29. Al Daoud, Essam. "Comparison between XGBoost, LightGBM and CatBoost Using a Home Credit Dataset." International Journal of Computer and Information Engineering 13.1 (2019): 6-10. [Google Scholar]
30. Chen, Ying, et al. "Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study." BMJ open 8.9 (2018): e021768. [view at publisher] [DOI] [Google Scholar]
31. Chen, Ying, et al. "Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study." BMJ open 8.9 (2018): e021768. [DOI]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
