Epidemiological Features of Human Brucellosis in Iran (2011-2018) and Prediction of Brucellosis with Data-Mining Models

Background: brucellosis is known as the major zoonotic disease. we aimed to compare the performance of some data-mining models in predicting the monthly brucellosis cases in iran. study design: population-based cohort study. methods: three data mining techniques including the support vector machine (svm), multivariate adaptive regression splines (mars), and random forest (rf) besides to one classic model including auto-regressive integrated moving average (arima) was used to predict the monthly incidence of brucellosis in iran during 2011-2018. we used several criteria (root mean square error (rmse), mean absolute error (mae), coefficient of determination (r2) and intra-class correlation coefficient (icc) for appraising the accuracy of prediction and performance of our models. all analysis was done using free statistical software of r3.4.0 results: overall 118867 cases (with a mean age of 34.01±1.65 yr) of brucellosis were observed and seven-year incidence rate of brucellosis in iran was 21.78 (95% ci: 21.66, 21.91). the majority of patients (58.84%) were male and 25-29 yr old. the first three provinces with the highest incidence rate of brucellosis included the following; kurdistan (71.39 per 100,000), lorestan (68.09 per 100,000) and hamadan (56.24 per 100,000). conclusion: brucellosis was more common in males, 25-29 aged yr, western provinces and spring months. the disease had a decreasing trend in the last years. mars model was more appropriate rather than data mining models for prediction of monthly incidence rate of brucellosis.