Abstract
In this research, we have examined type 2 diabetics treatment and medication detection using seven classifier algorithms. We have created a decision tree-based procedure with genetic and clinical features such as Fasting, 2 h after the glucose load, BMI, Duration (years), Age, gender-specific, and blood pressure for the treatment of type 2 diabetic patients. Medical treatment prevents some complications, but does not usually restore normoglycemia or remove all the adverse consequences. The tool here is to give a correct report to justify the right medications for a patient. Imparting a fivefold cross-validation process, the operation of applying clinical features of 666 type 2 diabetic patients in 7 classifiers Logistic Regression, Linear Discriminant Analysis, k-nearest neighbors, Decision Tree, Naive Bayes, support vector machine, and Random forest classifier. In this paper, this system support to change lifestyle and right medications for treatment, which assists to reduce the probability of type 2 diabetes in persons.
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Kowsher, M., Tithi, F.S., Rabeya, T., Afrin, F., Huda, M.N. (2020). Type 2 Diabetics Treatment and Medication Detection with Machine Learning Classifier Algorithm. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_44
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DOI: https://doi.org/10.1007/978-981-13-7564-4_44
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