Measurement of community beliefs about colorectal cancer

Few educational campaigns have focused on bowel cancer, though studies have indicated that members of the community need and want current information about relevant issues. In order to facilitate research in this area, reliable and valid measures of community attitudes are needed. Content validity of a survey instrument was obtained through use of a Delphi process with Directors of Education from the Australia Cancer Council and focus group discussions with informed members of the public. The subsequent survey of community perceptions about colorectal cancer included a broad range of content areas related to the risk of bowel cancer, preventing and coping with bowel cancer and beliefs about susceptibility and severity. The construct validity of these content areas was investigated by use of a factor analysis and confirmation of an association with related predictor variables. Two measures related to personal influence and anticipated coping responses showed favourable psychometric properties, including moderate to high levels of internal consistency and test-retest reliability. A test of the concurrent validity of these measures requires further development of instruments related to colorectal cancer or adaptation of measures from other areas of health research. (C) 2000 Elsevier Science Ltd. All rights reserved.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.