Classification of Bone Cements Using Multinomial Logistic Regression Method
PublicDownloadable Content
open in viewerBone cement surgery is a new technique widely used in medical field nowadays. In this thesis I analyze 48 bone cement types using their content of 20 elements. My goal is to find a method to classify new found bone cement sample into these 48 categories. Here I will use multinomial logistic regression method to see whether it works or not. Due to the lack of observations, I generate enough data by adding white noise in proper scales to the original data again and again, and then I get a data set of over 100 times as many points as the original one. Then I use purposeful variable selection method to pick the covariates I need, rather than stepwise selection. There are 15 covariates left after the selection, and then I use my new data set to fit such a multinomial logistic regression model. The model doesn't perform that good in goodness of fit test, but the result is still acceptable, and the diagnostic statistics also indicate a good performance. Combined with clinical experience and prior conditions, this model is helpful in this classification case.
- Creator
- Contributors
- Degree
- Unit
- Publisher
- Language
- English
- Identifier
- etd-042918-225713
- Keyword
- Advisor
- Defense date
- Year
- 2018
- Date created
- 2018-04-29
- Resource type
- Rights statement
- Last modified
- 2021-02-01
Relations
- In Collection:
Permanent link to this page: https://digital.wpi.edu/show/wh246s31g