Predicting diarrhoea outbreaks with climate change

Discussion The results of our experiments revealed that although the Deep Learning (DL) methods (Con- figuration of ML methods section) outperformed the SVM (SVM method section). In most tasks, there was no clear best ML method overall. The ML methods showed different levels of skill based on the availability of training data and the type of parameter tuning method used during training. Performance based on dataset type The CNN method (section CNN method) was able to generalize well and select important fea- tures to yield the most satisfactory performance when only real-world data was used for mak- ing predictions regardless of its limited training set size. Based on different metrics, some studies [19, 20] have shown results for CNNs to be more accurate than several other methods for infectious diseases prediction. We theorize this to be a result of CNNs being effective uni- versal approximators capable of automatic feature engineering [17]. Our findings also agree with previous research which showed that deep neural networks outperform traditional ML algorithms for most disease prediction tasks [19, 20]. Fig 9. Variable importance plot. Result of the sensitivity analysis carried out for the CNN prediction method for each province. The x-axis indicates the prediction accuracy of the method once the variable on the y-axis is omitted from the method. The longer the bar, the larger the loss in accuracy and the higher the importance of that variable. https://doi.org/10.1371/journal.pone.0262008.g009 PLOS ONE Predicting diarrhoea outbreaks with climate change

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