Helicobacter pylori (H. pylori) risk factor analysis and prevalence prediction: a machine learning-based approach.
Background: Although previous epidemiological studies have examined the potential risk factors that increase the likelihood of acquiring Helicobacter pylori infections, most of these analyses have utilized conventional statistical models, including logistic regression, and have not benefited from ad...
| Publicado en: | BMC Infectious Diseases Vol. 22; no. 1; pp. 1 - 15 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
7/28/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158238119&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158238119 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712334 1CHU jtl: BMC Infectious Diseases issn: 14712334 maglogo: N pubinfo: dt: 7/28/2022 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 158238119 158238119 NLM35902812 10.1186/s12879-022-07625-7 NLM35902812 158238119 ppf: 1 ppct: 14 formats: tig: atl: Helicobacter pylori (H. pylori) risk factor analysis and prevalence prediction: a machine learning-based approach. aug: au: Tran, Van Saad, Tazmilur Tesfaye, Mehret Walelign, Sosina Wordofa, Moges Abera, Dessie Desta, Kassu Tsegaye, Aster Ay, Ahmet Taye, Bineyam affil: Department of Mathematics, Colgate University, 13 Oak Dr., Hamilton, NY, USA sug: subj: Helicobacter Infections Epidemiology Helicobacter Pylori Algorithms Prevalence Factor Analysis Child Risk Factors Cross Sectional Studies Clinical Assessment Tools Child: 6-12 years ab: Background: Although previous epidemiological studies have examined the potential risk factors that increase the likelihood of acquiring Helicobacter pylori infections, most of these analyses have utilized conventional statistical models, including logistic regression, and have not benefited from advanced machine learning techniques.Objective: We examined H. pylori infection risk factors among school children using machine learning algorithms to identify important risk factors as well as to determine whether machine learning can be used to predict H. pylori infection status.Methods: We applied feature selection and classification algorithms to data from a school-based cross-sectional survey in Ethiopia. The data set included 954 school children with 27 sociodemographic and lifestyle variables. We conducted five runs of tenfold cross-validation on the data. We combined the results of these runs for each combination of feature selection (e.g., Information Gain) and classification (e.g., Support Vector Machines) algorithms.Results: The XGBoost classifier had the highest accuracy in predicting H. pylori infection status with an accuracy of 77%-a 13% improvement from the baseline accuracy of guessing the most frequent class (64% of the samples were H. Pylori negative.) K-Nearest Neighbors showed the worst performance across all classifiers. A similar performance was observed using the F1-score and area under the receiver operating curve (AUROC) classifier evaluation metrics. Among all features, place of residence (with urban residence increasing risk) was the most common risk factor for H. pylori infection, regardless of the feature selection method choice. Additionally, our machine learning algorithms identified other important risk factors for H. pylori infection, such as; electricity usage in the home, toilet type, and waste disposal location. Using a 75% cutoff for robustness, machine learning identified five of the eight significant features found by traditional multivariate logistic regression. However, when a lower robustness threshold is used, machine learning approaches identified more H. pylori risk factors than multivariate logistic regression and suggested risk factors not detected by logistic regression.Conclusion: This study provides evidence that machine learning approaches are positioned to uncover H. pylori infection risk factors and predict H. pylori infection status. These approaches identify similar risk factors and predict infection with comparable accuracy to logistic regression, thus they could be used as an alternative method. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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