PMID- 33077570
OWN - NLM
STAT- In-Process
LR  - 20201215
IS  - 2044-6055 (Electronic)
IS  - 2044-6055 (Linking)
VI  - 10
IP  - 10
DP  - 2020 Oct 19
TI  - Prediction of perinatal death using machine learning models: a birth registry-based 
      cohort study in northern Tanzania.
PG  - e040132
LID - 10.1136/bmjopen-2020-040132 [doi]
LID - e040132
AB  - 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.
CI  - © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by 
      BMJ.
FAU - Mboya, Innocent B
AU  - Mboya IB
AUID- ORCID: 0000-0001-9861-5879
AD  - School of Mathematics, Statistics, and Computer Science, University of 
      KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal, South Africa ib.mboya@gmail.com.
AD  - Department of Epidemiology and Biostatistics, Institute of Public Health, 
      Kilimanjaro Christian Medical University College, Moshi, Tanzania.
FAU - Mahande, Michael J
AU  - Mahande MJ
AUID- ORCID: 0000-0002-7750-7657
AD  - Department of Epidemiology and Biostatistics, Institute of Public Health, 
      Kilimanjaro Christian Medical University College, Moshi, Tanzania.
FAU - Mohammed, Mohanad
AU  - Mohammed M
AD  - School of Mathematics, Statistics, and Computer Science, University of 
      KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal, South Africa.
FAU - Obure, Joseph
AU  - Obure J
AD  - Department of Obstetrics and Gynecology, Kilimanjaro Christian Medical Center, 
      Moshi, Tanzania.
FAU - Mwambi, Henry G
AU  - Mwambi HG
AD  - School of Mathematics, Statistics, and Computer Science, University of 
      KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal, South Africa.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20201019
TA  - BMJ Open
JT  - BMJ open
JID - 101552874
SB  - IM
PMC - PMC7574940
OTO - NOTNLM
OT  - *epidemiology
OT  - *neonatology
OT  - *perinatology
OT  - *prenatal diagnosis
OT  - *reproductive medicine
COIS- Competing interests: None declared.
EDAT- 2020/10/21 06:00
MHDA- 2020/10/21 06:00
CRDT- 2020/10/20 06:17
PHST- 2020/10/20 06:17 [entrez]
PHST- 2020/10/21 06:00 [pubmed]
PHST- 2020/10/21 06:00 [medline]
AID - bmjopen-2020-040132 [pii]
AID - 10.1136/bmjopen-2020-040132 [doi]
PST - epublish
SO  - BMJ Open. 2020 Oct 19;10(10):e040132. doi: 10.1136/bmjopen-2020-040132.