Using machine learning algorithms to investigate factors associated with complete edentulism among older adults in the United States

Abimbola M Oladayo; Hikkaduwa Withanage Miyuraj Harishchandra; Erliang Zeng; Daniel J Caplan; Azeez Butali; Leonardo Marchini

doi:10.1111/scd.12832

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Using machine learning algorithms to investigate factors associated with complete edentulism among older adults in the United States

Journal article

Open access

Peer reviewed

Using machine learning algorithms to investigate factors associated with complete edentulism among older adults in the United States

Abimbola M Oladayo, Hikkaduwa Withanage Miyuraj Harishchandra, Erliang Zeng, Daniel J Caplan, Azeez Butali and Leonardo Marchini

Special care in dentistry, Vol.44(1), pp.148-156

01/2024

DOI: 10.1111/scd.12832

PMID: 36749021

Appears in UI Libraries Support Open Access

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Abstract

Edentulism is an incapacitating condition, and its prevalence is unequal among different population groups in the United States (US) despite its declining prevalence. This study aimed to investigate the current prevalence, apply Machine Learning (ML) Algorithms to investigate factors associated with complete tooth loss among older US adults, and compare the performance of the models. The cross-sectional 2020 Behavioral Risk Factor Surveillance System (BRFSS) data was used to evaluate the prevalence and factors associated with edentulism. ML models were developed to identify factors associated with edentulism utilizing seven ML algorithms. The performance of these models was compared using the area under the receiver operating characteristic curve (AUC). An overall prevalence of 11.9% was reported. The AdaBoost algorithm (AUC = 84.9%) showed the best performance. Analysis showed that the last dental visit, educational attainment, smoking, difficulty walking, and general health status were among the top factors associated with complete edentulism. Findings from our study support the declining prevalence of complete edentulism in older adults in the US and show that it is possible to develop a high-performing ML model to investigate the most important factors associated with edentulism using nationally representative data.

Machine Learning

older adults

tooth loss

BRFSS

UIOWA OA Agreement

Details

Title: Subtitle: Using machine learning algorithms to investigate factors associated with complete edentulism among older adults in the United States
Creators: Abimbola M Oladayo - University of Iowa
Hikkaduwa Withanage Miyuraj Harishchandra - Division of Biostatistics and Computational Biology, The University of Iowa College of Dentistry, Iowa City, Iowa, USA
Erliang Zeng - University of Iowa
Daniel J Caplan - University of Iowa
Azeez Butali - University of Iowa
Leonardo Marchini - University of Iowa
Resource Type: Journal article
Publication Details: Special care in dentistry, Vol.44(1), pp.148-156
DOI: 10.1111/scd.12832
PMID: 36749021
NLM abbreviation: Spec Care Dentist
eISSN: 1754-4505
Publisher: Wiley
Language: English
Electronic publication date: 02/07/2023
Date published: 01/2024
Academic Unit: Preventive and Community Dentistry; Roy J. Carver Department of Biomedical Engineering; Oral Pathology, Radiology and Medicine; Stead Family Department of Pediatrics; Iowa Neuroscience Institute; Biostatistics; Craniofacial Anomalies Research Center; Dental Research
Record Identifier: 9984365906202771

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