Implementation of Support Vector Machine on Text-based GERD Detection by using Drug Review Content

Asty Nabilah ’Izzaturrahmah, F. Nhita, I. Kurniawan
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引用次数: 2

Abstract

GERD or Gastroesophageal Reflux Disease is a situation when the reflux of stomach contents leads to unpleasant symptoms and/or complications. The prevalence range of GERD is approximately 18.1% to 27.8% in North America, 8.8% to 25.9% in Europe, 2.5% to 7.8% in East Asia, 8.7% to 33.1% in the Middle East, 11.6% in Australia, and 23.0% in South America. The numbers may seem small, but GERD will lead to several complications including esophagitis, peptic stricture, and Barrett’s esophagus if left untreated. The most common diagnostic test for the assessment of GERD along with its possible complications is the upper gastrointestinal endoscopy, or esophagogastroduodenoscopy (EGD). However, endoscopy has several risks. Disease detection using machine learning can be done and is needed due to the increment in medical data, new detection, and diagnostic modalities being developed. One of the machine learning algorithms often used in text classification is Support Vector Machine (SVM). This research applies SVM to do text-based classification, classifying data into two classes, namely GERD patient” and “not GERD patient using drug review data. The best model has 91.32% accuracy, 91% f1-score, and 91.32% AUC score with unigram as the n-gram range, and RBF with C is 1000, and gamma auto as the SVM kernel.
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支持向量机在基于文本的药品审查内容GERD检测中的实现
胃食管反流病是指胃内容物反流导致不愉快的症状和/或并发症。GERD患病率在北美约为18.1% ~ 27.8%,欧洲为8.8% ~ 25.9%,东亚为2.5% ~ 7.8%,中东为8.7% ~ 33.1%,澳大利亚为11.6%,南美为23.0%。这个数字可能看起来很小,但如果不及时治疗,反流会导致一些并发症,包括食管炎、消化性狭窄和巴雷特食管。评估GERD及其可能的并发症最常见的诊断测试是上消化道内窥镜或食管胃十二指肠镜(EGD)。然而,内窥镜检查有几个风险。由于医疗数据的增加、新的检测和诊断模式的开发,使用机器学习进行疾病检测是可以做到的,也是必要的。支持向量机(SVM)是文本分类中常用的机器学习算法之一。本研究采用SVM进行基于文本的分类,利用药物评审数据将数据分为“GERD患者”和“非GERD患者”两类。以uniggram为n-gram范围,RBF为1000,gamma auto为SVM核,最佳模型准确率为91.32%,f1分数为91%,AUC分数为91.32%。
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