| dc.contributor.author | You, Guangjing | |
| dc.description.abstract | In this study, the performance of Logistic Regression and Decision Tree modeling is compared by using SAS Enterprise Miner for predicting pre-diabetes in US population by using several of the common factors from the type 2 diabetes screening criteria. From 17 variables of NHANES’ three sets of dataset, a total of 13 risk factors were selected as predictors of pre-diabetes. A comparison of two data mining methodology showed that Decision Tree has a higher ROC index than Logistic Regression modeling. All ROC indexes for two models were greater than 77% indicating both methods present a good prediction for pre-diabetes. The predictive accuracy of the two models was greater than 72% on the whole dataset. Decision tree modeling also resulted in higher accuracy and sensitivity values than Logistic Regression modeling. Taken as a whole, the results of comparison indicated Decision Tree modeling is a better indicator to predict pre-diabetes. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU Policy 190.6.2 | |
| dc.title | Two Data Mining Applications for Predicting Pre-Diabetes | en_US |
| dc.type | Thesis | en_US |
| dc.description | Document incorrectly classified as a dissertation on title page (decision to classify as a thesis from NDSU Graduate School) | en_US |
| dc.date.accessioned | 2018-03-01T00:18:04Z | |
| dc.date.available | 2018-03-01T00:18:04Z | |
| dc.date.issued | 2015 | |
| dc.identifier.uri | https://hdl.handle.net/10365/27638 | |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Industrial and Manufacturing Engineering | en_US |
| ndsu.program | Industrial Engineering and Management | en_US |
| ndsu.advisor | Farahmand, Kambiz | |
