Conditional Forest Models Built Using Metagenomic Data Accurately Predicted Salmonella Contamination in Northeastern Streams

Chung, Taejung; Yan, Runan; Weller, Daniel; Kovač, Jasna

doi:10.1128/spectrum.00381-23

Cited by 1 publication

(1 citation statement)

References 61 publications

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“…It should be declared that all applied methods confirm each other due to the overlap in identifying the factors related to ANM. Past studies have indicated that predictions provided by Rforest are more accurate than Cforest [ 27 , 73 ], although in another study conditional forest outperformed the regularized random forest [ 74 ]. However, given the comparison of these 4 methods using MSE and correlation coefficient, unlike Rforest which was overfitted in the train set, the performance of Cforest was much better than other methods in the test set.…”

Section: Discussionmentioning

confidence: 99%

Age at natural menopause and its determinants in female population of Kharameh cohort study: Comparison of regression, conditional tree and forests

Pasokh,

Seif,

Ghaem

et al. 2024

PLoS ONE

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Background Natural menopause is defined as the permanent cessation of menstruation that occurs after 12 consecutive months of amenorrhea without any obvious pathological or physiological cause. The age of this phenomenon has been reported to be associated with several health outcomes. Objectives This study aimed to estimate the Age at Natural Menopause (ANM) and to identify reproductive and demographic factors affecting ANM. Methods This cross-sectional, population-based study was conducted on 2517 post-menopausal women aged 40–70 years participating in the first phase of the PERSIAN cohort study of Kharameh, Iran, during 2014–2017. To more accurately detect the determinants of ANM, we applied multiple linear regression beside some machine learning algorithms including conditional tree, conditional forest, and random forest. Then, the fitness of these methods was compared using Mean Squared Error (MSE) and Pearson correlation coefficient. Results The mean±SD of ANM was 48.95±6.13. Both applied forests provided more accurate results and identified more predictors. However, according to the final comparison, the conditional forest was the most accurate method which recognized that more pregnancies, longer breastfeeding, Fars ethnicity, and urbanization have the greatest impact on later ANM. Conclusions This study found a wide range of reproductive and demographic factors affecting ANM. Considering our findings in decision-making can reduce the complications related to this phenomenon and, consequently, improve the quality of life of post-menopausal women.

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Section: Discussionmentioning

confidence: 99%