A Nearest Neighbors Field Method Based on Distance for Missing Value Imputation in Medical Application

碩士 === 國立雲林科技大學 === 資訊管理系 === 106 === In medical filed, missing data is often existed, which will affect the analysis and prediction by doctors and scholars. Most studies have focused on the highest accuracy prediction model in current medical research. However, they do not have considered the stabi...

Full description

Bibliographic Details
Main Authors: Huang, Hao-Hsuan, 黃浩軒
Other Authors: Cheng, Ching-Hsue
Format: Others
Language:en_US
Published: 2018
Online Access:http://ndltd.ncl.edu.tw/handle/3a3f4d
Description
Summary:碩士 === 國立雲林科技大學 === 資訊管理系 === 106 === In medical filed, missing data is often existed, which will affect the analysis and prediction by doctors and scholars. Most studies have focused on the highest accuracy prediction model in current medical research. However, they do not have considered the stability of models with different missing degrees and different missing types. Based on data complete and easy operation, this study proposes the imputation method, which is nearest neighbors method based on distance to imputation missing value. In the experiment, this study selected several UCI datasets and produced different missing degrees and missing types. Comparing training accuracy with popular imputation methods. Moreover, using the Stroke dataset from the International Stroke Trial (IST) to verify whether the proposed method could be effectively used in practice. The results show that, the proposed method can have good performances under different simulations of missing degrees, missing types, and datasets. In addition, the proposed method can obtained 90-percentage accuracy in the Stoke dataset. It means the proposed method can be effectively used in the practice dataset.