title: Naive Bayes classifiers for verbal autopsies: comparison to physician-based classification for 21,000 child and adult deaths.
abstract: BACKGROUND
  Verbal autopsies (VA) are increasingly used in low- and middle-income countries where most causes of death (COD) occur at home without medical attention, and home deaths differ substantially from hospital deaths. Hence, there is no plausible "standard" against which VAs for home deaths may be validated. Previous studies have shown contradictory performance of automated methods compared to physician-based classification of CODs. We sought to compare the performance of the classic naive Bayes classifier (NBC) versus existing automated classifiers, using physician-based classification as the reference.
  
  
  METHODS
  We compared the performance of NBC, an open-source Tariff Method (OTM), and InterVA-4 on three datasets covering about 21,000 child and adult deaths: the ongoing Million Death Study in India, and health and demographic surveillance sites in Agincourt, South Africa and Matlab, Bangladesh. We applied several training and testing splits of the data to quantify the sensitivity and specificity compared to physician coding for individual CODs and to test the cause-specific mortality fractions at the population level.
  
  
  RESULTS
  The NBC achieved comparable sensitivity (median 0.51, range 0.48-0.58) to OTM (median 0.50, range 0.41-0.51), with InterVA-4 having lower sensitivity (median 0.43, range 0.36-0.47) in all three datasets, across all CODs. Consistency of CODs was comparable for NBC and InterVA-4 but lower for OTM. NBC and OTM achieved better performance when using a local rather than a non-local training dataset. At the population level, NBC scored the highest cause-specific mortality fraction accuracy across the datasets (median 0.88, range 0.87-0.93), followed by InterVA-4 (median 0.66, range 0.62-0.73) and OTM (median 0.57, range 0.42-0.58).
  
  
  CONCLUSIONS
  NBC outperforms current similar COD classifiers at the population level. Nevertheless, no current automated classifier adequately replicates physician classification for individual CODs. There is a need for further research on automated classifiers using local training and test data in diverse settings prior to recommending any replacement of physician-based classification of verbal autopsies.
authors.affiliation: Centre for Global Health Research, St. Michael's Hospital, Toronto, Ontario, Canada.
authors.forename: Pierre
authors.initials: P
authors.lastname: Miasnikof
authors.affiliation: Centre for Global Health Research, St. Michael's Hospital, Toronto, Ontario, Canada.
authors.forename: Vasily
authors.initials: V
authors.lastname: Giannakeas
authors.affiliation: Centre for Global Health Research, St. Michael's Hospital, Toronto, Ontario, Canada.
authors.forename: Mireille
authors.initials: M
authors.lastname: Gomes
authors.affiliation: Centre for Global Health Research, St. Michael's Hospital, Toronto, Ontario, Canada.
authors.forename: Lukasz
authors.initials: L
authors.lastname: Aleksandrowicz
authors.affiliation: Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Alexander Y
authors.initials: AY
authors.lastname: Shestopaloff
authors.affiliation: Centre for Global Health Research, St. Michael's Hospital, Toronto, Ontario, Canada.|Centre for Control of Chronic Diseases, International Centre for Diarrhoeal Diseases Research, Dhaka, Bangladesh.
authors.forename: Dewan
authors.initials: D
authors.lastname: Alam
authors.affiliation: Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
authors.forename: Stephen
authors.initials: S
authors.lastname: Tollman
authors.affiliation: Centre for Global Health Research, St. Michael's Hospital, Toronto, Ontario, Canada.|Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Ontario, Canada.
authors.forename: Akram
authors.initials: A
authors.lastname: Samarikhalaj
authors.affiliation: Centre for Global Health Research, St. Michael's Hospital, Toronto, Ontario, Canada. Prabhat.jha@utoronto.ca.|Dalla Lana School of Public Health, University of Toronto, Toronto, Canada. Prabhat.jha@utoronto.ca.
authors.forename: Prabhat
authors.initials: P
authors.lastname: Jha
mesh_terms: D000293:Adolescent; D000328:Adult; D001344:Autopsy / Q000592:standards* / Q000706:statistics & numerical data; D001499:Bayes Theorem; D002423:Cause of Death; D002648:Child; D002675:Child, Preschool; D003644:Death Certificates; D003710:Demography; D006801:Humans; D007182:Income / Q000706:statistics & numerical data; D007194:India / Q000453:epidemiology; D007223:Infant; D007231:Infant, Newborn; D008875:Middle Aged; D010820:Physicians; D013019:South Africa / Q000453:epidemiology; D055815:Young Adult