A machine learning approach to predict ethnicity using personal name and census location in Canada
Background Canada is an ethnically-diverse country, yet its lack of ethnicity information in many large databases impedes effective population research and interventions. Automated ethnicity classification using machine learning has shown potential to address this data gap but its performance in Can...
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ftdoajarticles:oai:doaj.org/article:faa01e9d0d23465faac1f201211aea85 2023-05-15T16:16:54+02:00 A machine learning approach to predict ethnicity using personal name and census location in Canada Kai On Wong Osmar R. Zaïane Faith G. Davis Yutaka Yasui Sreeram V. Ramagopalan 2020-01-01T00:00:00Z https://doaj.org/article/faa01e9d0d23465faac1f201211aea85 EN eng Public Library of Science (PLoS) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673495/?tool=EBI https://doaj.org/toc/1932-6203 1932-6203 https://doaj.org/article/faa01e9d0d23465faac1f201211aea85 PLoS ONE, Vol 15, Iss 11 (2020) Medicine R Science Q article 2020 ftdoajarticles 2022-12-31T01:22:56Z Background Canada is an ethnically-diverse country, yet its lack of ethnicity information in many large databases impedes effective population research and interventions. Automated ethnicity classification using machine learning has shown potential to address this data gap but its performance in Canada is largely unknown. This study conducted a large-scale machine learning framework to predict ethnicity using a novel set of name and census location features. Methods Using census 1901, the multiclass and binary class classification machine learning pipelines were developed. The 13 ethnic categories examined were Aboriginal (First Nations, Métis, Inuit, and all-combined)), Chinese, English, French, Irish, Italian, Japanese, Russian, Scottish, and others. Machine learning algorithms included regularized logistic regression, C-support vector, and naïve Bayes classifiers. Name features consisted of the entire name string, substrings, double-metaphones, and various name-entity patterns, while location features consisted of the entire location string and substrings of province, district, and subdistrict. Predictive performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, F1, Area Under the Curve for Receiver Operating Characteristic curve, and accuracy. Results The census had 4,812,958 unique individuals. For multiclass classification, the highest performance achieved was 76% F1 and 91% accuracy. For binary classifications for Chinese, French, Italian, Japanese, Russian, and others, the F1 ranged 68–95% (median 87%). The lower performance for English, Irish, and Scottish (F1 ranged 63–67%) was likely due to their shared cultural and linguistic heritage. Adding census location features to the name-based models strongly improved the prediction in Aboriginal classification (F1 increased from 50% to 84%). Conclusions The automated machine learning approach using only name and census location features can predict the ethnicity of Canadians with varying performance by ... Article in Journal/Newspaper First Nations inuit Directory of Open Access Journals: DOAJ Articles Canada |
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Medicine R Science Q |
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Medicine R Science Q Kai On Wong Osmar R. Zaïane Faith G. Davis Yutaka Yasui Sreeram V. Ramagopalan A machine learning approach to predict ethnicity using personal name and census location in Canada |
topic_facet |
Medicine R Science Q |
description |
Background Canada is an ethnically-diverse country, yet its lack of ethnicity information in many large databases impedes effective population research and interventions. Automated ethnicity classification using machine learning has shown potential to address this data gap but its performance in Canada is largely unknown. This study conducted a large-scale machine learning framework to predict ethnicity using a novel set of name and census location features. Methods Using census 1901, the multiclass and binary class classification machine learning pipelines were developed. The 13 ethnic categories examined were Aboriginal (First Nations, Métis, Inuit, and all-combined)), Chinese, English, French, Irish, Italian, Japanese, Russian, Scottish, and others. Machine learning algorithms included regularized logistic regression, C-support vector, and naïve Bayes classifiers. Name features consisted of the entire name string, substrings, double-metaphones, and various name-entity patterns, while location features consisted of the entire location string and substrings of province, district, and subdistrict. Predictive performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, F1, Area Under the Curve for Receiver Operating Characteristic curve, and accuracy. Results The census had 4,812,958 unique individuals. For multiclass classification, the highest performance achieved was 76% F1 and 91% accuracy. For binary classifications for Chinese, French, Italian, Japanese, Russian, and others, the F1 ranged 68–95% (median 87%). The lower performance for English, Irish, and Scottish (F1 ranged 63–67%) was likely due to their shared cultural and linguistic heritage. Adding census location features to the name-based models strongly improved the prediction in Aboriginal classification (F1 increased from 50% to 84%). Conclusions The automated machine learning approach using only name and census location features can predict the ethnicity of Canadians with varying performance by ... |
format |
Article in Journal/Newspaper |
author |
Kai On Wong Osmar R. Zaïane Faith G. Davis Yutaka Yasui Sreeram V. Ramagopalan |
author_facet |
Kai On Wong Osmar R. Zaïane Faith G. Davis Yutaka Yasui Sreeram V. Ramagopalan |
author_sort |
Kai On Wong |
title |
A machine learning approach to predict ethnicity using personal name and census location in Canada |
title_short |
A machine learning approach to predict ethnicity using personal name and census location in Canada |
title_full |
A machine learning approach to predict ethnicity using personal name and census location in Canada |
title_fullStr |
A machine learning approach to predict ethnicity using personal name and census location in Canada |
title_full_unstemmed |
A machine learning approach to predict ethnicity using personal name and census location in Canada |
title_sort |
machine learning approach to predict ethnicity using personal name and census location in canada |
publisher |
Public Library of Science (PLoS) |
publishDate |
2020 |
url |
https://doaj.org/article/faa01e9d0d23465faac1f201211aea85 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
First Nations inuit |
genre_facet |
First Nations inuit |
op_source |
PLoS ONE, Vol 15, Iss 11 (2020) |
op_relation |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673495/?tool=EBI https://doaj.org/toc/1932-6203 1932-6203 https://doaj.org/article/faa01e9d0d23465faac1f201211aea85 |
_version_ |
1766002748709404672 |