Use of ICD (International Classification of Diseases) to prepare medical data in decision support systems

Data quality is very crucial for the success Data analysis.The data loaded to the data warehouse must be correct, accurate and must be of very high quality.High quality data in the data warehouse will result in the better analysis and better decision making.So this data quality issues must be addressed before the data is loaded in to the data warehouse. Data cleaning find errors and remove errors.It also detect and deals with data redundancy and data inconsistency. Data cleaning using ETL to ensure quality data in the data warehouse for effective business intelligence. The purpose of data cleansing is to detect so called dirty data (incorrect, irrelevant or incomplete parts of the data) to either modify or delete it to ensure that a given set of data is accurate and consistent with other sets in the system. This research aims to explain and clarify data cleaning method for correcting dirty data. The sample database was defined as the collection of all diseases in the provinces of Zanjan, Elam and Hamedan. In order to solve the problems in the sample database C # and SQL store Procedure was applied. An important part of the results revealed the error after data cleaning was reduced to 0.008 %.