Table 1.

Classification performance of various ML algorithms on seasonal respiratory infections and symptoms at the household level (all population).

Method	Accuracy		Precision		Recall		F1-score
	Mean	Standard error	Mean	Standard error	Mean	Standard error	Mean	Standard error
Decision tree	69.453	0.012	76.892	0.010	75.651	0.010	76.261	0.009
Random forest	73.519	0.005	76.456	0.004	85.523	0.007	80.725	0.004
XGBoost	74.167	0.006	76.497	0.006	86.922	0.010	81.353	0.005
AdaBoost	72.523	0.007	74.944	0.005	86.590	0.007	80.342	0.005
SVM	73.346	0.005	75.576	0.004	87.058	0.008	80.900	0.004
Neural network	70.185	0.006	76.439	0.010	78.524	0.025	77.274	0.009

Accuracy: Accuracy measures a classification model’s performance by calculating the ratio of correctly predicted instances to total cases. It provides an overall assessment of the model’s ability to classify data correctly.

Precision: Precision is a metric that evaluates how well a classification model identifies positive instances. It represents the ratio of correctly predicted positive cases to the total number of cases predicted as positive. Precision is essential when minimizing false positives is critical.

Recall: Recall measures how effectively a model captures all the relevant positive cases and is critical when the goal is to minimize the number of missed positive instances.

F1-score: The F1-score is a machine learning metric that assesses a classification model’s performance, especially in cases with imbalanced datasets, by combining precision and recall into a single measure.

The mean values represent the average performance of the model across the fivefold cross-validation, reported as percentages. The standard error (SE) indicates the variability of the performance measures across the different folds, with lower SE values indicating more consistent model performance. For more details, refer to the Methods section.