A new procedure for variable selection in presence of rare events

Giordano, Francesco; Niglio, Marcella; Restaino, Marialuisa

doi:10.1080/01605682.2020.1740620

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…The logistic regression model on low prevalence data clearly under-estimates the general probability [14]. A similar phenomenon was also observed for the Lasso based model, albeit with signi cantly smaller under-estimation.…”

Section: Discussionsupporting

confidence: 72%

Nomogram to Early Screen Multiparous Women for Preterm Birth in a Cohort Study

Traboulsi

Talibi

Boussaid

2020

Preprint

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Background: Preterm Birth (PTB) can negatively affect the health of mothers as well as infants. Prediction of this gynecological complication remains difficult especially in Middle and Low-Income countries because of limited access to specific tests and data collection scarcity. Multiparous women in our study presented a higher PTB prevalence compared to nulliparous women. Methods: In a cohort study from Northern Lebanon of 1996 women, 922 were multiparous presenting a PTB prevalence of 8%. We analyzed the personal, demographic, and health indicators available for this group of women. We compared 4 modified logistic regression models (up-sampling, lasso penalized regression) to develop a nomogram that can screen for preterm in multi-parous women. The models were validated on a separate set of data.Results: The best PTB prediction of the Logistic regression model reached around 88%. This was obtained using a Logistic Regression Model trained on up-sampled datasets and LASSO (Least Absolute Shrinkage and Selection Operator) penalized. The regression coefficients of the 6 selected variables (Pre-hemorrhage, Social status, Residence, Age, BMI, and Weight gain) were used to create a nomogram to screen multiparous women for PTB risk. Conclusions: The nomogram based on readily available indicators for multiparous women reasonably predicted most of the at PTB risk women. This tool will allow physicians to screen women and run furthermore adequate additional tests leading to better medical surveillance that can reduce PTB incidence.

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Section: Discussionsupporting

confidence: 72%

Nomogram to Early Screen Multiparous Women for Preterm Birth in a Cohort Study

Traboulsi

Talibi

Boussaid

2020

Preprint

View full text Add to dashboard Cite

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“…This technique has been successfully used in investigations with low or very low prevalence, including some machine learning techniques such as convolutional neural networks [9]. The logistic regression model on low prevalence data clearly under-estimates the general probability [19]. A similar phenomenon was also observed for the Lasso based model, albeit with significantly smaller under-estimation.…”

Section: Discussionmentioning

confidence: 63%

Nomogram to Early Screen Multiparous Women for Preterm Birth in a Cohort Study

Traboulsi

Talibi

Boussaid

2021

International Journal of Biology and Biomedical Engineering

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Preterm Birth (PTB) can negatively affect the health of mothers as well as infants. Prediction of this gynecological complication remains difficult especially in Middle and Low-Income countries because of limited access to specific tests and data collection scarcity. Machine learning methods have been used to predict PTB but the low prevalence of this pregnancy complication led to rather low prediction values. The objective of this study was to produce a nomogram based on improved prediction for low prevalence PTB using up sampling and lasso penalized regression. We used data from a cohort study in Northern Lebanon of 922 multiparous presenting a PTB prevalence of 8%. We analyzed the personal, demographic, and health indicators available for this group of women. The improved Positive Predictive Value for PTB reached around 88%. The regression coefficients of the 6 selected variables (Pre-hemorrhage, Social status, Residence, Age, BMI, and Weight gain) were used to create a nomogram to screen multiparous women for PTB risk. The nomogram based on readily available indicators for multiparous women reasonably predicted most of the at PTB risk women. The physicians can use this tool to screen for women at high risk for spontaneous preterm birth to improve medical surveillance that can reduce PTB incidence.

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