A Logistic Regression-Based Technique for Predicting Type II Diabetes
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Date
2024-05-11
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Computational Sciences & Informatics
Abstract
In recent years, diabetes has emerged as one of the main causes of death for people. Thespread of unhealthy foods, sedentary lifestyles, and eating habits have all contributed tothe annual increase in the incidence of diabetes. A diabetes prediction model can help withclinical management decision-making. Diabetes prevention may be aided by being awareof potential risk factors and early detection of high-risk individuals. Numerous diabetesprediction models have been created. The size of the data set to be used was an issue inearlier research, but more recent studies have incorporated the use of high-qualitytrustworthy data sets, such as the Vanderbilt and PIMA India data sets. Recent researchhas 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 Idiabetes. Machine learning models of these parameters can be used to accurately predictthe chance of the disease occurring as it was investigated in this study. In order to predictType II diabetes, this study used the machine learning method Logistic Regression.
Keywords: Type II Diabetes, Logistic Regression, Hyperparameter and Prediction