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Uddip Acharjee Shuvo on Muck Rack

Uddip Acharjee Shuvo

As seen in: PLOS ONE, BioRxiv
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Articles

Correction: Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach

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By Probir Kumar Ghosh, Alamgir Hossain, Uddip Acharjee Shuvo, Fatema khatun
| PLOS ONE
Loading metrics The Introduction heading has been misspelled. The correct spelling is Introduction. Reference 1. Das NC, Ghosh PK, Hossain MA, Shuvo UA, Talukder NR, Khatun F, et al. Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach. PLoS One. 2025;20(10):e0335442. pmid:41166313 View Article PubMed/NCBI Google Scholar Citation: Das NC, Ghosh PK, Hossain MA, Shuvo UA, Talukder NR, Khatun F, et al.
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Correction: Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach

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By Probir Kumar Ghosh, Alamgir Hossain, Uddip Acharjee Shuvo, Fatema khatun
| PLOS ONE
Loading metrics The Introduction heading has been misspelled. The correct spelling is Introduction. Reference 1. Das NC, Ghosh PK, Hossain MA, Shuvo UA, Talukder NR, Khatun F, et al. Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach. PLoS One. 2025;20(10):e0335442. pmid:41166313 View Article PubMed/NCBI Google Scholar Citation: Das NC, Ghosh PK, Hossain MA, Shuvo UA, Talukder NR, Khatun F, et al.
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Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach

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By Probir Kumar Ghosh, Alamgir Hossain, Uddip Acharjee Shuvo, Fatema khatun
| PLOS ONE
Methodology We have used Bangladesh Demographic and Health Survey (BDHS) 2017−18 data in this study, which is the nationally representative survey. The National Institute of Population Research and Training, Medical Education and Family Welfare Division and Ministry of Health and Family Welfare jointly conducted the survey from October 2017 to March 2018.
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