Hadi Bagheri1, Leili Tapak2, Manoochehr Karami1, Behzad Amiri3, Zahra Cheraghi4. 1. Department of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. 2. Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. 3. Centers for Communicable Disease Control and Prevention, Ministry of Health and Medical Education, Tehran, Iran. 4. Department of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. cheraghiz@ymail.com.
Abstract
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 100000), Lorestan (68.09 per 100000) and Hamadan (56.24 per 100000). 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.
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 100000), Lorestan (68.09 per 100000) and Hamadan (56.24 per 100000). 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.
Entities:
Keywords:
Brucellosis; Cohort studies; Data mining; Iran
Authors: Samuel Dixon; Ravikiran Keshavamurthy; Daniel H Farber; Andrew Stevens; Karl T Pazdernik; Lauren E Charles Journal: Pathogens Date: 2022-01-29