Title:	APPLICATION OF SLIDING WINDOWS TO SPELLING ERROR DETECTION IN MEDICAL DIAGNOSIS
Author(s):	Sutat Gammanee and Sunantha Sodsee
ISBN:	978-989-8704-21-4
Editors:	Yingcai Xiao, Ajith P. Abraham and Jörg Roth
Year:	2020
Edition:	Single
Keywords:	Self-Diagnosis, Spelling Errors Detection, Sliding Windows
Type:	Full
First Page:	149
Last Page:	156
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Presently, problematic issues relative to health and public health in Thailand are underlined by an insufficient number of medical personnel. However, self-diagnosis, herein, is another option to help the mentioned limitation by identifying and monitoring unusual health conditions by oneself. To gain the diagnosis, history of medical data based on symptoms and disease of patients are needed significantly. In fact, especially in Thailand, medical data is recorded, not only presented by, such as: numeric data, images, but also describing by words or phrases. Then, the errors of medical data collection may be occurred by incorrect data or spelling errors of describing. To enhance the performance of self-diagnosis, in this paper, the medical data in describing by medical words is focused. The detection of spelling errors and measuring similar words are coped with the proposed sliding window based methods, which are 1) sliding window based two-word inspection by once sliding method 2) sliding window based two-word inspection by initial word fixing method, and 3) sliding window based window size adjusting method. For evaluating the proposed work, the example medical data (without patient information) is gained from the public hospital in Thailand. The results presented that the sliding window based two-word inspection by initial word fixing method presented the great accuracy of spelling errors detection about 80% in general words and 60 % of similar words measurement in medical terms.
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