Prediction of leptospirosis cases using classification algorithms
Prediction of leptospirosis cases using classification algorithms
复制标题
使用分类算法预测钩端螺旋体病病例
DOI：
10.1049/iet-sen.2016.0193
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
J. Lindow
中科院分区：
文献类型：
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作者：
N. Nery;Daniela Barreiro Claro;J. Lindow
钩端螺旋体病是一种潜在危及生命的疾病，主要影响低收入人群，估计全球每年有 103 万人感染。这种疾病的症状经常与其他发热综合征（例如登革热、流感和病毒性肝炎）混淆，常常使诊断变得困难。提高钩端螺旋体病患者早期诊断的准确性将加快适当抗生素治疗的速度，两者都将改善这种潜在致命疾病的临床结果。作者对临床和流行病学定义的钩端螺旋体病病例进行了分析，以使用数据挖掘分类算法预测疾病。他们进行了四组实验来评估算法的性能，评估它们使用不同训练和测试数据集的预测准确性。 JRIP 算法使用仅确认的钩端螺旋体病病例的数据集实现了 84% 的灵敏度，使用仅确认的登革热病例的数据集实现了 99% 的特异性。因此，该方法成功预测了钩端螺旋体病病例，将其与类似的发热性疾病区分开来，并可能成为协助卫生专业人员（特别是在钩端螺旋体病流行地区）的新工具，加速有针对性的治疗并最大限度地减少疾病恶化和死亡率。
Leptospirosis is a potentially life-threatening disease primarily affecting low-income populations, with an estimated annual incidence of 1.03 million infections worldwide. This disease has symptoms often confused with other febrile syndromes, such as dengue fever, influenza and viral hepatitis, often making diagnosis challenging. Improving the accuracy of early diagnosis of patients with leptospirosis will increase the speed of appropriate antibiotic treatment delivery, and both will improve clinical outcomes for this potentially fatal disease. The authors conducted an analysis of clinically and epidemiologically defined leptospirosis cases to predict disease using data mining classification algorithms. They conducted four sets of experiments to evaluate the performance of the algorithms, assessing their predictive accuracy of using different training and test datasets. The JRIP algorithm achieved 84% sensitivity using a dataset of only confirmed leptospirosis cases, and a specificity of 99% using a dataset of only confirmed dengue cases. Therefore, the approach successfully predicted leptospirosis cases, differentiated them from similar febrile illnesses, and may represent a new tool to assist health professionals, particularly in endemic areas for leptospirosis, accelerating targeted treatment and minimising disease exacerbation and mortality.