Various classifications for caesarian section classification dataset data set
نویسندگان
چکیده
منابع مشابه
Endometriosis at caesarian section scar.
Endometriosis is a common gynecological condition which is sometimes presented to general surgeons as a lump in the abdomen. It can pose a diagnostic dilemma and should be in the differential diagnosis of lumps in the abdomen in females. Diagnosis is usually made following histological examination. This is a case report of abdominal wall endometriosis following caesarian section. This report di...
متن کاملSingle dose antacid therapy before caesarian section.
The severity of pulmonary aspiration depends mainly on the acidity of the aspirate. Mist. magnesium trisilicate (MMT) has been used for many years at the maternity unit in General Hospital, Kuala Lumpur, to neutralise the acidic gastric contents in all obstetric patients requiring caesarian section. This preliminary study shows that a single dose of 15 mls of MMT before general anaesthesia rais...
متن کاملDataset Vertical Partitioning for Rough Set Based Classification
Dataset partitioning problem involves the vertical partitioning of the classification datasets into suitable subsets that preserve or enhance the classification quality of the original datasets. Typical classification model needs to be constructed for each subset and all generated models are then combined to form the classification model. This paper presents a dataset partitioning approach for ...
متن کاملClassifications for Cesarean Section: A Systematic Review
BACKGROUND Rising cesarean section (CS) rates are a major public health concern and cause worldwide debates. To propose and implement effective measures to reduce or increase CS rates where necessary requires an appropriate classification. Despite several existing CS classifications, there has not yet been a systematic review of these. This study aimed to 1) identify the main CS classifications...
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ژورنال
عنوان ژورنال: Indian Journal of Computer Science and Engineering
سال: 2018
ISSN: 2231-3850,0976-5166
DOI: 10.21817/indjcse/2018/v9i6/180906013