Instituto Federal de Educao, Ciência e Tecnología da Bahia, Campus Vitória da Conquista, Bahia, Brasil.
Escuela Técnica Superior de Ingeniería Informática, Universidad de Málaga, Málaga, Spain.
J Med Syst. 2017 Aug 11;41(9):145. doi: 10.1007/s10916-017-0785-5.
This paper describes the development of statistical classifiers to help diagnose meningococcal meningitis, i.e. the most sever, infectious and deadliest type of this disease. The goal is to find a mechanism able to determine whether a patient has this type of meningitis from a set of symptoms that can be directly observed in the earliest stages of this pathology. Currently, in Brazil, a country that is heavily affected by meningitis, all suspected cases require immediate hospitalization and the beginning of a treatment with invasive tests and medicines. This procedure, therefore, entails expensive treatments unaffordable in less developed regions. For this purpose, we have gathered together a dataset of 22,602 records of suspected meningitis cases from the Brazilian state of Bahia. Seven classification techniques have been applied from input data of nine symptoms and other information about the patient such as age, sex and the area they live in, and a 10 cross-fold validation has been performed. Results show that the techniques applied are suitable for diagnosing the meningococcal meningitis. Several indexes, such as precision, recall or ROC area, have been computed to show the accuracy of the models. All of them provide good results, but the best corresponds to the J48 classifier with a precision of 0.942 and a ROC area over 0.95. These results indicate that our model can indeed help lead to a non-invasive and early diagnosis of this pathology. This is especially useful in less developed areas, where the epidemiologic risk is usually high and medical expenses, sometimes, unaffordable.
本文描述了统计分类器的开发，以帮助诊断脑膜炎球菌性脑膜炎，即这种疾病中最严重、最具传染性和最致命的类型。目标是找到一种机制，能够根据在这种疾病的早期阶段可以直接观察到的一组症状来确定患者是否患有这种类型的脑膜炎。目前，在巴西这个受脑膜炎影响严重的国家，所有疑似病例都需要立即住院，并开始进行侵入性测试和药物治疗。因此，这种程序需要昂贵的治疗，在欠发达地区是无法承受的。为此，我们收集了来自巴西巴伊亚州的 22602 例疑似脑膜炎病例的数据。从九个症状的输入数据以及患者的其他信息（如年龄、性别和居住地区）应用了七种分类技术，并进行了十次交叉验证。结果表明，所应用的技术适合诊断脑膜炎球菌性脑膜炎。计算了几个指标，如精度、召回率或 ROC 面积，以显示模型的准确性。所有这些都提供了很好的结果，但最好的是 J48 分类器，其精度为 0.942，ROC 面积超过 0.95。这些结果表明，我们的模型确实可以帮助实现这种病理学的非侵入性和早期诊断。这在欠发达地区尤其有用，那里的流行病学风险通常很高，医疗费用有时难以承受。
