用逻辑回归预测儿童疟疾严重程度，准确率达83.3%。
A Logistic Regression Model to Predict Malaria Severity in Children
- 基于贫血、积水、垃圾堆等10项因素构建逻辑回归模型。
- 在加纳博索姆特韦区417名儿童中实现83.3%的预测准确率。
- 强调样本类别代表性对模型性能的关键作用。
全球范围内，疟疾是主要死亡原因之一。研究人员曾尝试基于气象、气候及疟原虫繁殖周期数据建立疟疾暴发预测模型。本研究聚焦于基于环境与生物因素预测儿童疟疾严重程度。采用逻辑回归模型，纳入镰状细胞病、积水、垃圾堆、潮湿草坪及蚊帐使用等变量，在加纳博索姆特韦区417名受试者中取得83.3%的准确率。研究发现尽管该地区儿童疟疾感染率高，但病情普遍较轻。研究强调，机器学习模型开发不仅需足够样本量，更需各类别样本具有代表性。
原文摘要 · Abstract (English)
One of the main causes of death around the globe is malaria. Researchers have sought to develop predictive models for malaria outbreaks based on meteorological data, climate data and the breeding cycle of Plasmodium, the causative agent of malaria. This study predicts the severity of malaria based on environmental and biological factors. A logistic regression model was developed in this study to predict the severity of malaria based on such factors as sickle cell disease, stagnant water, garbage dump, wet lawns, and the use of treated mosquito nets, with an 83.3% accuracy rate. The study was carried out in the Bosomtwe District of Ghana with 417 respondents. It was deduced that although children in the District are highly prone to malaria infection, the severity is very low. The study recommends that not just having a good sample size alone is important during machine learning model development, but also having a good sample representation of the various class labels is equally important.
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