id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-261043-f9w310tp	Ajayi, Toluwalope	Forecasting herd-level porcine epidemic diarrhea (PED) frequency trends in Ontario (Canada)	2019-03-01		.txt	text/plain	6517	277	45	With recent advances in predictive analytics showing promise for health and disease forecasting, the primary objective of this study was to apply machine learning predictive methods (random forest, artificial neural networks, and classification and regression trees) to provincial PEDV incidence data, and in so doing determine their accuracy for predicting future PEDV trends. With 10-fold cross validation performed on the entire dataset, the overall accuracy was 0.68 (95% CI: 0.60 – 0.75), 0.57 (95% CI: 0.49 – 0.64), and 0.55 (0.47 – 0.63) for the random forest, artificial neural network, and classification and regression tree models, respectively. For the additional models constructed with random training and test sets (using 10-fold cross validation on the entire dataset), the summary confusion matrix in Table 3 indicates overall accuracy values of 68%, 57%, and 55% for random forest, neural nets, and classification trees respectively.	./cache/cord-261043-f9w310tp.txt	./txt/cord-261043-f9w310tp.txt