Prediction of Diabetes at Early Stage Using Machine Learning Algorithms
DOI:
https://doi.org/10.54691/bcpbm.v38i.3978Keywords:
Diabetes; early stage; machine learning.Abstract
Diabetes is a chronic illness caused by a lack of insulin produced in the pancreas, which leads to an elevated blood glucose level. Diabetes leads to complications, such as loss of vision and kidney function, heart attacks, and strokes, which could ultimately shorten life. Diabetes has many different symptoms, for example, obesity, polydipsia, and itching. This study aimed to use these symptoms to predict diabetes using machine learning algorithms and to find the best classifier. The result is that random forest had the highest accuracy of 97% on this dataset.
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Anjali D Deshpande, Marcie Harris-Hayes, Mario Schootman, Epidemiology of Diabetes and Diabetes-Related Complications, Physical Therapy, Volume 88, Issue 11, 1 November 2008, Pages 1254–1264, https://doi.org/10.2522/ptj.20080020.
"Diabetes.” World Health Organization, 16 Sep, 2022, https://www.who.int/news-room/fact- sheets/detail/diabetes.
"What is diabetes?” cdc.gov, https://www.cdc.gov/diabetes/basics/diabetes.html. Ac- cessed 14 August 2022.
Factors, Risk. ”Early Warning: A Look at the First Signs of Diabetes.” TrustCare, 27 Sep, 2021, https://trustcarehealth.com/blog/early-warning-a-look-at-the-first-signs-of-diabetes.
Latha, Asha. “XGBoost as Regressor and Classifier.” Inblog, 20 Jan, 2021, blog.ineuron.ai/XGBoost- as-Regressor-and-Classifier-4IVqLERTn8.
”9–DecisionTree.”https://wizardforcel.gitbooks.io/ntu-hsuantienlin-ml/content/27.html. Accessed 11 September 2022.
”LogisticRegressioninMachineLearning.”https://www.javatpoint.com/logistic-regression- in-machine-learning. Accessed 11 September 2022.
R, Sruthi. ”Understanding Random Forest.” Analytics Vidhya, 21 June, 2022, https://www.analyticsvidhya.com random-forest/.
Harrison, Onel. ”Machine Learning Basics with the K-Nearest Neighbors Algorithm.” To- wards Data Science, 10 September, 2022, https://towardsdatascience.com/machine-learning- basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761.
Roche, Edna Frances, et al. “Clinical Presentation of Type 1 Diabetes.” Pediatric Diabetes, vol. 6, no. 2, 2005, pp. 75–78., https://doi.org/10.1111/j.1399-543x.2005.00110.x.
Catherine C. Cowie, Keith F. Rust, Earl S. Ford, Mark S. Eberhardt, Danita D. Byrd- Holt, Chaoyang Li, Desmond E. Williams, Edward W. Gregg, Kathleen E. Bainbridge, Sharon H. Saydah, Linda S. Geiss; Full Accounting of Diabetes and Pre-Diabetes in the U.S. Population in 1988–1994 and 2005–2006. Diabetes Care 1 February 2009; 32 (2): 287–294. https://doi.org/10.2337/dc08-1296.
Babakinejad, Parastoo, and Shernaz Walton. “Diabetes and Pruritus.” British Journal of Diabetes, vol. 16, no. 4, 2016, p. 154., https://doi.org/10.15277/bjd.2016.095.
Hjart ̊aker, A., Langseth, H., Weiderpass, E. (2008). Obesity and Diabetes Epidemics:. In: Berstein, L.M., Santen, R.J. (eds) Innovative Endocrinology of Cancer. Advances in Exper- imental Medicine and Biology, vol 630. Springer, New York, NY. https://doi.org/10.1007/978- 0-387-78818-0 6.
Babakinejad, Parastoo, and Shernaz Walton. “Diabetes and Pruritus.” British Journal of Diabetes, vol. 16, no. 4, 2016, p. 154., https://doi.org/10.15277/bjd.2016.095.
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