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Prediction of leptospirosis cases using classification algorithms

机译：使用分类算法预测钩端螺旋体病病例

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摘要

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.
机译：钩端螺旋体病是一种潜在的威胁生命的疾病，主要影响低收入人群，估计全世界每年感染的发病率为103万例。这种疾病的症状通常与其他高热综合征相混淆，例如登革热，流感和病毒性肝炎，常常使诊断具有挑战性。提高钩端螺旋体病患者早期诊断的准确性将提高适当抗生素治疗的速度，并且两者都将改善这种潜在致命疾病的临床疗效。作者使用数据挖掘分类算法对临床和流行病学确定的钩端螺旋体病病例进行了分析，以预测疾病。他们进行了四组实验以评估算法的性能，评估使用不同训练和测试数据集的预测准确性。使用仅确诊的钩端螺旋体病病例的数据集，JRIP算法可达到84％的灵敏度，而仅仅确诊的登革热病例的数据集的特异性为99％。因此，该方法成功地预测了钩端螺旋体病病例，将它们与类似的高热疾病区分开来，并且可能代表了一种新的工具，可以帮助卫生专业人员，尤其是钩端螺旋体病的流行地区，加快针对性治疗并最大程度地降低疾病加重和死亡率。

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