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Risk prediction of household mite infestation based on machine learning

机译：基于机器学习的家庭螨虫害的风险预测

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House dust mites produce the allergens causing allergic diseases. Predicting risk level of mites infestation through environmental conditions instead of using complex detection methods can increasing people's attention to mites and avoid huge detection cost. Mite allergens Der 1 (Der f 1 + Der p 1) in 101 residential apartments in different regions of China were measured. Indoor environmental parameters were continuously monitored and occupant surveys were also collected. Der 1 over 2000 ng/g is defined as mite infestation risk level. Compared with logistic regression and support vector machine (SVM), the prediction results of the extremely gradient boosting (XGBoost) model are more accurate (ACC = 0.838, Recall = 0.844) and highly interpretable, thus it is the most suitable method for mite risk level prediction. Indoor environment data of other cities in China were collected for prediction, "risk level map of mite infestation in China" was produced. The predicted results showed that risk level of mite infestation in southern China are generally higher than in northern cities. Based on the well-trained XGBoost prediction model, the relationship between input features and probability of mite infestation reaching risk level (P-R=1) can be found: 1) "mite active breeding zone" was defined with temperature between 11 and 30 degrees C and relative humidity 53%-80%, where P-R=1 exceeds 70%. 2) Increasing frequency of cleaning (both washing cleaning and vacuum cleaning) is effective for reducing risk level of mite infestation, but in humid environments (where relative humidity exceeds 53%) the effectiveness of cleaning is limited.
机译：房屋尘螨产生引起过敏性疾病的过敏原。通过环境条件预测螨虫感染的风险水平，而不是使用复杂的检测方法可以提高人们对螨虫的注意力并避免巨大的检测成本。测量了中国不同地区的101公寓中的101公寓中的螨虫Allergens（der F 1 + der p 1）。不断监测室内环境参数，并收集乘员调查。 Der 1超过2000 ng / g被定义为螨虫侵扰风险水平。与Logistic回归和支持向量机（SVM）相比，极梯度升压（XGBoost）模型的预测结果更准确（ACC = 0.838，召回= 0.844）和高度可解释，因此它是螨虫风险最合适的方法水平预测。中国其他城市的室内环境数据被收集了预测，“中国螨虫害的风险水平地图”是制作的。预测结果表明，中国南方螨虫害的风险水平通常高于北方城市。基于训练有素的XGBoost预测模型，可以找到输入特征与触发风险级别（PR = 1）的概率之间的关系：1）“螨虫活动育种区”定义在11到30摄氏度之间的温度之间相对湿度53％-80％，其中Pr = 1超过70％。 2）增加清洁频率（洗涤清洁和真空清洁）可有效降低螨虫害的风险水平，但在潮湿环境中（相对湿度超过53％），清洁的有效性有限。

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