应用朴素贝叶斯算法诊断发热症状

Triyanna Widiyaningtyas, I. Zaeni, Nadiratin Jamilah
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引用次数: 3

摘要

登革出血热(DHF)和斑疹伤寒(TF)是具有相似症状的疾病。登革出血热的发烧是由埃及伊蚊叮咬引起的,而登革热的发烧是由伤寒沙门氏菌引起的。这两种疾病的症状相似,往往会导致患者的误诊,由于处理不当,可能导致患者病情恶化。为了克服这个问题,我们需要一种方法来诊断这两种疾病的发烧症状。在数据挖掘中,疾病的诊断可以通过分类技术来完成。诊断发烧症状的分类过程是使用Naïve贝叶斯算法。算法测试使用k-fold交叉验证完成,k等于10。通过计算预测结果的准确度、精密度和召回率来衡量算法的评价。结果表明,平均准确率为94%,精密度为90%,召回率为92%。这表明Naïve贝叶斯算法在诊断患者发热方面具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diagnosis of fever symptoms using naive bayes algorithm
Dengue Hemorrhagic Fever (DHF) and Typhus Fever (TF) are diseases that have similar symptoms. Fever in DHF is caused by the bite of the Aedes Aegypti mosquito, whereas fever in TF is caused by the bacterium Salmonella Typhi. The similarity of symptoms in these two diseases often leads to misdiagnosis of the patient, which can cause the patient's condition to worsen due to incorrect handling. To overcome this problem, we need a method to diagnose the symptoms of fever in both diseases. In data mining, the diagnosis of the disease can be done by classification techniques. The classification process for diagnosing fever symptoms is using the Naïve Bayes algorithm. Algorithm testing is done using k-fold cross-validation, with k equal to 10. The evaluation of the algorithm is measured by calculating the value of accuracy, precision, and recall from prediction results. The results showed that the average accuracy rate was 94%, precision was 90%, and recall was 92%. This shows that the Naïve Bayes algorithm has good performance in diagnosing fever in patients.
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