TAILIEUCHUNG - Imputation Methods to Deal with Missing Values when Data Mining Trauma Injury Data

Methods for analysing trauma injury data with missing values, collected at a UK hospital, are reported. One measure of injury severity, the Glasgow coma score, which is known to be associated with patient death, is missing for 12% of patients in the dataset. In order to include these 12% of patients in the analysis, three different data imputation techniques are used to estimate the missing values. The imputed data sets are analysed by an artificial neural network and logistic regression, and their results compared in terms of sensitivity, specificity, positive predictive value and negative predictive value | Imputation Methods to Deal with Missing Values when Data Mining Trauma Injury Data Kay I Penny Centre for Mathematics and Statistics Napier University Craiglockhart Campus Edinburgh EH14 1DJ Thomas Chesney Nottingham University Business School Jubilee Campus Wollaton Road Nottingham NG81BB Abstract. Methods for analysing trauma injury data with missing values collected at a UK hospital are reported. One measure of injury severity the Glasgow coma score which is known to be associated with patient death is missing for 12 of patients in the dataset. In order to include these 12 of patients in the analysis three different data imputation techniques are used to estimate the missing values. The imputed data sets are analysed by an artificial neural network and logistic regression and their results compared in terms of sensitivity specificity positive predictive value and negative predictive value. Keywords Data mining missing data imputation trauma injury. 1. Introduction Trauma injury is the most common cause of loss of life to those under forty 1 . In 1991 a trauma system was put in place at the North Staffordshire Hospital NSH in Stoke-on-Trent in the . It records injury details including Injury Severity Score ISS 2 Abbreviated Injury Scores AIS 3 the Glasgow Coma Score GCS 4 the patient s sex and age management and interventions and the outcome of the treatment including whether the patient lived or died during their hospital stay. North Staffordshire Hospital is a major trauma centre in the area and receives patient referrals from surrounding hospitals. Oakley 5 analysed data for only the most severely injured patients admitted between 1992 to 1998 and found determinants of mortality for this subset of patients included age head AIS chest AIS abdominal AIS external injury AIS mechanism of injury primary receiving hospital and calendar year of admission. Further analysis includes a comparison of several .

• Cơ sở dữ liệu
• Data mining
• missing data imputation
• trauma injury
• Imputation Methods
• Missing Values
• Trauma Injury Data
• Data warehousing and data mining
• Data warehousing
• Data warehouse modelling
• Data mining functionalities
• Association rule mining
• FP growth algorithm
• K Nearest neighbor classification
• Lecture Data mining
• Bài giảng Khai thác dữ liệu
• Data mining for data streams
• Mining data streams
• Stream frequent pattern analysis
• Khai phá dữ liệu
• Overview of data mining
• Association rules
• Other Data Mining problems
• Applications of data mining
• Sequence data
• Sequential pattern mining
• Sequential pattern mining algorithms
• Data stream mining
• Data stream processing algorithms
• Cash register model
• Time series model
• Lecture Data mining and analysis
• Khai thác dữ liệu
• Data analysis foundations
• Frequent pattern mining
• Itemset mining
• Sequence mining
• Graph pattern mining
• Data warehouses
• On line analytical processing
• Association rules mining
• Mining association rules
• Computational complexity
• Frequent itemset generation
• Mining frequent closed patterns
• Data warehouse
• Data warehouse architecture
• Data warehouse implementation
• Data warehouse—subject oriented
• Graph data
• High dimensional data
• Numeric attributes
• Categorical attributes
• Kernel methods
• Dimensionality reduction
• Summarizing itemsets
• Rule assessment