Prediction of perinatal death using machine learning models : A birth registry-based cohort study in northern Tanzania
Mboya, Innocent B. LU
; Mahande, Michael J.
; Mohammed, Mohanad
; Obure, Joseph
and Mwambi, Henry G.
(2020)
In BMJ Open
10(10).
p.1-11
- Abstract
Objective We aimed to determine the key predictors of perinatal deaths using machine learning models compared with the logistic regression model. Design A secondary data analysis using the Kilimanjaro Christian Medical Centre (KCMC) Medical Birth Registry cohort from 2000 to 2015. We assessed the discriminative ability of models using the area under the receiver operating characteristics curve (AUC) and the net benefit using decision curve analysis. Setting The KCMC is a zonal referral hospital located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. The Medical Birth Registry is within the hospital grounds at the Reproductive and Child Health Centre. Participants Singleton deliveries (n=42 319) with complete records from... (More)
Objective We aimed to determine the key predictors of perinatal deaths using machine learning models compared with the logistic regression model. Design A secondary data analysis using the Kilimanjaro Christian Medical Centre (KCMC) Medical Birth Registry cohort from 2000 to 2015. We assessed the discriminative ability of models using the area under the receiver operating characteristics curve (AUC) and the net benefit using decision curve analysis. Setting The KCMC is a zonal referral hospital located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. The Medical Birth Registry is within the hospital grounds at the Reproductive and Child Health Centre. Participants Singleton deliveries (n=42 319) with complete records from 2000 to 2015. Primary outcome measures Perinatal death (composite of stillbirths and early neonatal deaths). These outcomes were only captured before mothers were discharged from the hospital. Results The proportion of perinatal deaths was 3.7%. There were no statistically significant differences in the predictive performance of four machine learning models except for bagging, which had a significantly lower performance (AUC 0.76, 95% CI 0.74 to 0.79, p=0.006) compared with the logistic regression model (AUC 0.78, 95% CI 0.76 to 0.81). However, in the decision curve analysis, the machine learning models had a higher net benefit (ie, the correct classification of perinatal deaths considering a trade-off between false-negatives and false-positives) - over the logistic regression model across a range of threshold probability values. Conclusions In this cohort, there was no significant difference in the prediction of perinatal deaths between machine learning and logistic regression models, except for bagging. The machine learning models had a higher net benefit, as its predictive ability of perinatal death was considerably superior over the logistic regression model. The machine learning models, as demonstrated by our study, can be used to improve the prediction of perinatal deaths and triage for women at risk.
(Less)
- author
- Mboya, Innocent B.
LU
; Mahande, Michael J.
; Mohammed, Mohanad
; Obure, Joseph
and Mwambi, Henry G.
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- keywords
- epidemiology, neonatology, perinatology, prenatal diagnosis, reproductive medicine
- in
- BMJ Open
- volume
- 10
- issue
- 10
- article number
- e040132
- pages
- 1 - 11
- publisher
- BMJ Publishing Group
- external identifiers
-
- scopus:85093911498
- pmid:33077570
- ISSN
- 2044-6055
- DOI
- 10.1136/bmjopen-2020-040132
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: ©
- id
- 7f7a70dc-6bd9-43b3-94dc-0b2f35f79d0c
- date added to LUP
- 2022-09-29 10:06:30
- date last changed
- 2024-05-02 15:45:00
@article{7f7a70dc-6bd9-43b3-94dc-0b2f35f79d0c,
abstract = {{<p>Objective We aimed to determine the key predictors of perinatal deaths using machine learning models compared with the logistic regression model. Design A secondary data analysis using the Kilimanjaro Christian Medical Centre (KCMC) Medical Birth Registry cohort from 2000 to 2015. We assessed the discriminative ability of models using the area under the receiver operating characteristics curve (AUC) and the net benefit using decision curve analysis. Setting The KCMC is a zonal referral hospital located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. The Medical Birth Registry is within the hospital grounds at the Reproductive and Child Health Centre. Participants Singleton deliveries (n=42 319) with complete records from 2000 to 2015. Primary outcome measures Perinatal death (composite of stillbirths and early neonatal deaths). These outcomes were only captured before mothers were discharged from the hospital. Results The proportion of perinatal deaths was 3.7%. There were no statistically significant differences in the predictive performance of four machine learning models except for bagging, which had a significantly lower performance (AUC 0.76, 95% CI 0.74 to 0.79, p=0.006) compared with the logistic regression model (AUC 0.78, 95% CI 0.76 to 0.81). However, in the decision curve analysis, the machine learning models had a higher net benefit (ie, the correct classification of perinatal deaths considering a trade-off between false-negatives and false-positives) - over the logistic regression model across a range of threshold probability values. Conclusions In this cohort, there was no significant difference in the prediction of perinatal deaths between machine learning and logistic regression models, except for bagging. The machine learning models had a higher net benefit, as its predictive ability of perinatal death was considerably superior over the logistic regression model. The machine learning models, as demonstrated by our study, can be used to improve the prediction of perinatal deaths and triage for women at risk. </p>}},
author = {{Mboya, Innocent B. and Mahande, Michael J. and Mohammed, Mohanad and Obure, Joseph and Mwambi, Henry G.}},
issn = {{2044-6055}},
keywords = {{epidemiology; neonatology; perinatology; prenatal diagnosis; reproductive medicine}},
language = {{eng}},
number = {{10}},
pages = {{1--11}},
publisher = {{BMJ Publishing Group}},
series = {{BMJ Open}},
title = {{Prediction of perinatal death using machine learning models : A birth registry-based cohort study in northern Tanzania}},
url = {{http://dx.doi.org/10.1136/bmjopen-2020-040132}},
doi = {{10.1136/bmjopen-2020-040132}},
volume = {{10}},
year = {{2020}},
}