Evidence map›Paper›PMID 32291361›Full record

ArticleJournal of research in health sciences2019

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

Hadi Bagheri, Leili Tapak, Manoochehr Karami, Behzad Amiri, Zahra Cheraghi

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Article in Journal of research in health sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

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20citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hadi BagheriDepartment of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Leili TapakDepartment of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Manoochehr KaramiDepartment of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Behzad AmiriCenters for Communicable Disease Control and Prevention, Ministry of Health and Medical Education, Tehran, Iran.
Zahra CheraghiDepartment of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. cheraghiz@ymail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBrucellosis 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

designPopulation-based cohort study.

methodsThree 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

resultsOverall 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).

conclusionBrucellosis 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.

Indexed as

Models, StatisticalSupport Vector MachineAdultAnimalsBrucellosisData MiningFemaleHumansIncidenceIranMaleMiddle AgedSeasonsZoonosesBrucellosisCohort studiesData miningIran

Identifiers

PMID32291361
PMCPMC7183567

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.