Lu Bingqian, Li Yanni, Evans Ciaran
Data Science Institute, Columbia University, New York, New York, United States.
Department of Statistical Sciences, Wake Forest University, Winston-Salem, North Carolina, United States.
PLoS One. 2025 Jun 3;20(6):e0323886. doi: 10.1371/journal.pone.0323886. eCollection 2025.
Early diagnosis of dengue fever is important for individual treatment and monitoring disease prevalence in the population. To assist diagnosis, previous studies have proposed classification models to detect dengue from symptoms and clinical measurements. However, there has been little exploration of whether existing models can be used to make predictions for new populations. In this study, we assess the generalizability of dengue classification models to new datasets. We trained logistic regression models on five publicly available dengue datasets from previous studies, using three explanatory variables identified as important in prior work: age, white blood cell count, and platelet count. These five datasets were collected at different times in different locations, with a variety of disease rates and patient ages. A model was trained on each dataset, and predictive performance and model calibration was evaluated on both the original (training) dataset, and the other (test) datasets from different studies. By comparing the model's performance when applied to data from a new location, we are able to assess the model's generalizability to new populations. We further compared performance with larger models and other classification methods. In-sample area under the receiver operating characteristic curve (AUC) values for the logistic regression models ranged from 0.74 to 0.89, while out-of-sample AUCs ranged from 0.55 to 0.89. Matching age ranges in training/test datasets increased AUC values and balanced the sensitivity and specificity. Adjusting the predicted probabilities to account for differences in dengue prevalence improved calibration in 20/28 training-test pairs. Results were similar when other explanatory variables were included and when other classification methods (decision trees and support vector machines) were used. The in-sample performance of the logistic regression model was consistent with previous dengue classifiers, suggesting the chosen model is a good choice in a variety of settings and has decent overall performance. However, adjustments are required to make predictions on new datasets. Practitioners can use existing dengue classifiers in new settings but should be careful with different patient ages and disease rates.
登革热的早期诊断对于个体治疗以及监测人群中的疾病流行情况至关重要。为辅助诊断，先前的研究提出了分类模型，以便从症状和临床测量数据中检测登革热。然而，对于现有模型能否用于对新人群进行预测，却鲜有探索。在本研究中，我们评估了登革热分类模型对新数据集的泛化能力。我们使用先前研究中确定为重要的三个解释变量（年龄、白细胞计数和血小板计数），在五个公开可用的登革热数据集上训练逻辑回归模型。这五个数据集是在不同时间、不同地点收集的，具有不同的疾病发生率和患者年龄。在每个数据集上训练一个模型，并在原始（训练）数据集以及来自不同研究的其他（测试）数据集上评估预测性能和模型校准。通过比较模型应用于新地点数据时的性能，我们能够评估模型对新人群的泛化能力。我们还将性能与更大的模型和其他分类方法进行了比较。逻辑回归模型在样本内的受试者工作特征曲线下面积（AUC）值范围为0.74至0.89，而样本外AUC值范围为0.55至0.89。在训练/测试数据集中匹配年龄范围可提高AUC值，并平衡敏感性和特异性。调整预测概率以考虑登革热患病率的差异，在20/28个训练-测试对中改善了校准。当纳入其他解释变量以及使用其他分类方法（决策树和支持向量机）时，结果相似。逻辑回归模型的样本内性能与先前的登革热分类器一致，表明所选模型在各种情况下都是一个不错的选择，并且具有良好的整体性能。然而，对新数据集进行预测需要进行调整。从业者可以在新环境中使用现有的登革热分类器，但对于不同的患者年龄和疾病发生率应谨慎对待。
