استفاده از طبقه‌بندی بین‌المللی آماری بیماری‌ها (ICD)برای آماده‌سازی داده‌های پزشکی در سیستمهای پشتیبان تصمیم
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند
فروغ السادات حسینی
1
,
مهدی افضلی
2
,
محمود مرادی
3
1 - دانشگاه علوم پزشکی و خدمات درمانی و بهداشتی، زنجان، ایران
2 - . استادیار، گروه مهندسی فن‌آوری اطلاعات، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران
3 - استادیار، گروه علم اطلاعات و دانش شناختی، دانشگاه رازی، کرمانشاه، ایران
کلید واژه: ICD, انباره داده, ILTEC, آماده‌سازی داده, هوش‌تجاری,
چکیده مقاله :
کیفیت اطلاعات در موفقیت تجزیه و تحلیل اطلاعات بسیار حیاتی و مهم است. اطلاعات بارگذاری شده در انبارداده باید صحیح، دقیق و با کیفیت باشد. داده با کیفیت در انباره داده موجب تحلیل مناسب و تصمیم‌گیری بهتر می‌‌شود. همچنین مباحث کیفیت داده باید قبل از بارگذاری در انباره داده مورد توجه قرار بگیرد. پاکسازی داده به مفهوم یافتن و حذف خطاها است. همچنین در این فرایند داده های اضافی و ناسازگار شناسایی می‌شوند. پاکسازی داده در مرحله استخراج، انتقال، بارگذاری با اطمینان از کیفیت داده در انباره داده موجبات اثربخشی هوشمندی کسب و کار را فراهم می‌آورد. هدف پاکسازی‌داده، شناسایی داده‌های بد (اشتباه، نامرتبط و ناقص) به منظور اصلاح یا حذف آن‌ها است تا از دقت و سازگاری مجموعه داده اطمینان حاصل شود. این پژوهش با هدف تشریح و تبیین روش پاکسازی داده برای حذف داده‌های بد انجام شده است. بانک‌اطلاعاتی نمونه از اطلاعات بیماری‌های استان‌های زنجان، ایلام وهمدان تشکیل شده است. به منظور حل مشکلات داده در بانک نمونه از فرم سی‌شارپ و ابزاهای نرم افزار اس‌کیوال استفاده شده است. بخش اصلی نتایج نشان می دهد که بکارگیری روش پاکسازی داده موجب کاهش میزان خطای بانک داده تا میزان 008/0 درصد شده است.
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 %.
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