A Logistic Regression-Based Technique for Predicting Type II Diabetes.
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Date
2024-01-29
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Published by Academic City University College, Accra, Ghana.
Abstract
In recent years, diabetes has emerged as one of the main causes of death for people. The
spread of unhealthy foods, sedentary lifestyles, and eating habits have all contributed to
the annual increase in the incidence of diabetes. A diabetes prediction model can help with
clinical management decision-making. Diabetes prevention may be aided by being aware
of potential risk factors and early detection of high-risk individuals. Numerous diabetes
prediction models have been created. The size of the data set to be used was an issue in
earlier research, but more recent studies have incorporated the use of high-quality,
trustworthy data sets, such as the Vanderbilt and PIMA India data sets. Recent research
has demonstrated that a few variables, including glucose, pregnancy, body mass index
(BMI), the function of the diabetic pedigree, and age, can be used to predict Type II
diabetes. Machine learning models of these parameters can be used to accurately predict
the chance of the disease occurring as it was investigated in this study. In order to predict
Type II diabetes, this study used the machine learning method Logistic Regression.
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Citation
Babatunde R. S. et. al., 2024