A Logistic Regression-Based Technique for Predicting Type II Diabetes
| dc.contributor.author | Ronke Seyi Babatunde | |
| dc.contributor.author | Akinbowale Nathaniel Babatunde | |
| dc.contributor.author | Shuaib Babatunde Mohammed | |
| dc.date.accessioned | 2025-04-07T13:02:41Z | |
| dc.date.available | 2025-04-07T13:02:41Z | |
| dc.date.issued | 2024 | |
| dc.description.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. | |
| dc.identifier.citation | 3 | |
| dc.identifier.uri | https://kwasuspace.kwasu.edu.ng/handle/123456789/4967 | |
| dc.language.iso | en | |
| dc.publisher | Journal of The Faculty of Computational Sciences & Informatics | |
| dc.title | A Logistic Regression-Based Technique for Predicting Type II Diabetes | |
| dc.type | Article |