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Objective: Public health authorities monitor epidemiological syndromes to provide early alerts of anomalies. A variety of approaches are applied for effective surveillance systems for influenza like illness (ILI). The present study systematically scores the accuracy of algorithms used for automated and prospective infectious-disease-outbreak detection. Another objective is to improve the performance of machine-learning (ML) approaches through statistical learning. Material and Methods: In order to reflect various situations, the volume and the size of the outbreak is chosen different for each simulation. We simulate 20 yearly sets of "daily ILI visit" to emergency department (ED), which includes seasonal outbreaks as well as unusual outbreaks of varying duration and magnitude. We search which biosurveillance algorithms work best across hidden "unusual outbreaks". Results: In terms of timeliness, both settings of kNN (res-raw), RF (resraw), and LR-raw have the best performance. All ML algorithms have sensitivity results greater than 0.90, where SVM-res (0.97), EWMA (0.96), CUSUM-moderate (0.95) are the best algorithms in terms of specificity. ML algorithms all give better performance with an integrated fitted regression model. The methods which have high sensitivity and specificity together is SVM-res (0.90 and 0.97), and LR-res (0.92 and 0.83). Conclusion: The results verified that ML algorithms integrated with statistical methods can be applied to daily ED data and can be used as a real-time surveillance method for prospective monitoring of ILI cases in the emergency setting. This study can contribute to the early detection of hidden unusual outbreaks for epidemiological studies. [ABSTRACT FROM AUTHOR]