ABSTRACT
Type II diabetes is a chronic metabolic disease secondary to elevated blood glucose levels. Complications of this disease include heart attack, stroke, blindness, renal failure, lower limb amputation and mortality. Due to its rising prevalence and consequent mortality, it is important to identify at an early stage those patients at high risk of developing diabetes. We applied 8 machine learning techniques namely: support vector machine, logistic regression, k-nearest neighbor, naïve Bayes, decision tree, random forest, AdaBoost and XGBoost in predicting diabetes using a publicly available diabetes dataset. In our study, Naïve Bayes with median imputation and recursive feature elimination obtained the highest performance with an accuracy rate of 81.0%. Although the results are very promising, one major limitation in this study is the small number of samples in the dataset. Early accurate detection can help patients to proactively monitor their lifestyle habits mitigating the risks of complications of uncontrolled diabetes.
Supplemental Material
Available for Download
Presentation slides
- B. Shamreen Ahamed and Dr. Meenakshi Sumeet Arya. 2021. LGBM Classifier based Technique for Predicting Type-2 Diabetes. Eur. J. Mol. & Clin. Med. 8, 3 (2021), 454–467. Retrieved from https://ejmcm.com/article_9403.htmlGoogle Scholar
- Fayroza Alaa Khaleel and Abbas M. Al-Bakry. 2021. Diagnosis of diabetes using machine learning algorithms. Mater. Today Proc. (July 2021). DOI:https://doi.org/10.1016/j.matpr.2021.07.196Google Scholar
- Manal Alghamdi, Mouaz Al-Mallah, Steven Keteyian, Clinton Brawner, Jonathan Ehrman, and Sherif Sakr. 2017. Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project. PLoS One 12, 7 (July 2017), e0179805. DOI:https://doi.org/10.1371/journal.pone.0179805Google ScholarCross Ref
- Vandana C Bavkar and Arundhati A Shinde. 2021. Machine learning algorithms for Diabetes prediction and neural network method for blood glucose measurement. Indian J. Sci. Technol. 14, 10 (March 2021), 869–880. DOI:https://doi.org/10.17485/IJST/v14i10.2187Google ScholarCross Ref
- Henock M. Deberneh and Intaek Kim. 2021. Prediction of Type 2 Diabetes Based on Machine Learning Algorithm. Int. J. Environ. Res. Public Health 18, 6 (March 2021), 3317. DOI:https://doi.org/10.3390/ijerph18063317Google Scholar
- Yixiang Deng, Lu Lu, Laura Aponte, Angeliki M. Angelidi, Vera Novak, George Em Karniadakis, and Christos S. Mantzoros. 2021. Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients. npj Digit. Med. 4, 1 (December 2021), 109. DOI:https://doi.org/10.1038/s41746-021-00480-xGoogle Scholar
- María Teresa García-Ordás, Carmen Benavides, José Alberto Benítez-Andrades, Héctor Alaiz-Moretón, and Isaías García-Rodríguez. 2021. Diabetes detection using deep learning techniques with oversampling and feature augmentation. Comput. Methods Programs Biomed. 202, (April 2021), 105968. DOI:https://doi.org/10.1016/j.cmpb.2021.105968Google Scholar
- Niranjana Murthy H.S. 2021. Early Prognosis of Diabetes Using Supervised Learning Techniques: A Comparison of Performance. Rev. Gestão Inovação e Tecnol. 11, 4 (July 2021), 140–148. DOI:https://doi.org/10.47059/revistageintec.v11i4.2098Google Scholar
- Janus Christian Jakobsen, Christian Gluud, Jørn Wetterslev, and Per Winkel. 2017. When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med. Res. Methodol. 17, 1 (December 2017), 162. DOI:https://doi.org/10.1186/s12874-017-0442-1Google ScholarCross Ref
- Satish Kumar Kalagotla, Suryakanth V. Gangashetty, and Kanuri Giridhar. 2021. A novel stacking technique for prediction of diabetes. Comput. Biol. Med. 135, (August 2021), 104554. DOI:https://doi.org/10.1016/j.compbiomed.2021.104554Google ScholarDigital Library
- Jobeda Jamal Khanam and Simon Y. Foo. 2021. A comparison of machine learning algorithms for diabetes prediction. ICT Express 7, 4 (December 2021), 432–439. DOI:https://doi.org/10.1016/j.icte.2021.02.004Google ScholarCross Ref
- Saloni Kumari, Deepika Kumar, and Mamta Mittal. 2021. An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. Int. J. Cogn. Comput. Eng. 2, (June 2021), 40–46. DOI:https://doi.org/10.1016/j.ijcce.2021.01.001Google Scholar
- Mingqi Li, Xiaoyang Fu, and Dongdong Li. 2020. Diabetes Prediction Based on XGBoost Algorithm. IOP Conf. Ser. Mater. Sci. Eng. 768, 7 (March 2020), 072093. DOI:https://doi.org/10.1088/1757-899X/768/7/072093Google ScholarCross Ref
- Kyra Mikaela M. Lopez and Ma. Sheila A. Magboo. 2020. A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers. . IOS Press, Seoul, South Korea. DOI:https://doi.org/10.3233/FAIA200765Google Scholar
- Ma. Sheila A. Magboo and Andrei D. Coronel. 2019. Data Mining Electronic Health Records to Support Evidence-Based Clinical Decisions. . 223–232. DOI:https://doi.org/10.1007/978-981-13-8566-7_22Google Scholar
- Ma. Sheila A. Magboo and Andrei D. Coronel. 2019. 30-Day Hospital Readmission Prediction Model for Diabetic Patients within the 30-70 Age Group. Proc. Acad. World 130 th Int. Conf. Madrid, Spain, 10 th -11 th June, 2019 (2019), 1–8. Retrieved from https://www.worldresearchlibrary.org/up_proc/pdf/2968-15656902101-8.pdfGoogle Scholar
- Vincent Peter C. Magboo and Ma. Sheila A. Magboo. 2021. Machine Learning Classifiers on Breast Cancer Recurrences. Procedia Comput. Sci. 192, (2021), 2742–2752. DOI:https://doi.org/10.1016/j.procs.2021.09.044Google ScholarDigital Library
- Anna Paleczek, Dominik Grochala, and Artur Rydosz. 2021. Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection. Sensors 21, 12 (June 2021), 4187. DOI:https://doi.org/10.3390/s21124187Google ScholarCross Ref
- Harsh Jigneshkumar Patel, Parita Oza, and Smita Agrawal. 2021. Diabetes Prediction Using Machine Learning. In Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, Pradeep Kumar Singh, Sawomir T. and Wierzcho, Sudeep and Tanwar, Maria and Ganzha and Joel J. P. C. and Rodrigues (eds.). Springer Singapore, Singapore, 703–715. DOI:https://doi.org/10.1007/978-981-16-0733-2_50Google Scholar
- Anju Prabha, Jyoti Yadav, Asha Rani, and Vijander Singh. 2021. Design of intelligent diabetes mellitus detection system using hybrid feature selection based XGBoost classifier. Comput. Biol. Med. 136, (September 2021), 104664. DOI:https://doi.org/10.1016/j.compbiomed.2021.104664Google ScholarDigital Library
- Anant Ram and Honey Vishwakarma. 2021. Diabetes Prediction using Machine learning and Data Mining Methods. IOP Conf. Ser. Mater. Sci. Eng. 1116, 1 (April 2021), 012135. DOI:https://doi.org/10.1088/1757-899X/1116/1/012135Google ScholarCross Ref
- Jayroop Ramesh, Raafat Aburukba, and Assim Sagahyroon. 2021. A remote healthcare monitoring framework for diabetes prediction using machine learning. Healthc. Technol. Lett. 8, 3 (June 2021), 45–57. DOI:https://doi.org/10.1049/htl2.12010Google ScholarCross Ref
- Vandana Rawat and Suryakant. 2019. A Classification System for Diabetic Patients with Machine Learning Techniques. Int. J. Math. Eng. Manag. Sci. 4, 3 (June 2019), 729–744. DOI:https://doi.org/10.33889/IJMEMS.2019.4.3-057Google Scholar
- R Saxena, S K Sharma, and M Gupta. 2021. Analysis of machine learning algorithms in diabetes mellitus prediction. J. Phys. Conf. Ser. 1921, 1 (May 2021), 012073. DOI:https://doi.org/10.1088/1742-6596/1921/1/012073Google ScholarCross Ref
- Salliah Shafi and Gufran Ahmad Ansari. 2021. Early Prediction of Diabetes Disease & Classification of Algorithms Using Machine Learning Approach. SSRN Electron. J. (2021). DOI:https://doi.org/10.2139/ssrn.3852590Google Scholar
- S Sivaranjani, S Ananya, J Aravinth, and R Karthika. 2021. Diabetes Prediction using Machine Learning Algorithms with Feature Selection and Dimensionality Reduction. In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, 141–146. DOI:https://doi.org/10.1109/ICACCS51430.2021.9441935Google ScholarCross Ref
- Jiby T C. 2021. A Study on Various Machine Learning Classification Algorithms for Diabetes Prediction. Int. J. Eng. Res. Technol. 10, 8 (2021), 425–427. Retrieved from https://www.ijert.org/a-study-on-various-machine-learning-classification-algorithms-for-diabetes-predictionGoogle Scholar
- Pelin Yildirim Taser. 2021. Application of Bagging and Boosting Approaches Using Decision Tree-Based Algorithms in Diabetes Risk Prediction. Proceedings 74, 1 (March 2021), 6. DOI:https://doi.org/10.3390/proceedings2021074006Google Scholar
- Diabetes. Retrieved from https://www.who.int/news-room/fact-sheets/detail/diabetesGoogle Scholar
Recommendations
Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers
The Diabetes-Mellitus (DM) disease is considered a persistent ailment that is triggered by excessive sugar levels in the blood of a person. It gives rise to severe health complications when left untreated and can also give rise to related diseases such as ...
Prediction of cardiovascular risk by measuring carotid intima media thickness from an ultrasound image for type II diabetic mellitus subjects using machine learning and transfer learning techniques
AbstractCardiovascular disease (CVD) is a fatal disease that causes increased death in developing and developed nations. Among the various reasons, the increase in carotid intima media thickness (CIMT) is also a significant reason for CVD. It is expected ...
Comments