Download This Paper
Preprints with The Lancet is a collaboration between The Lancet Group of journals and SSRN to facilitate the open sharing of preprints for early engagement, community comment, and collaboration. Preprints available here are not Lancet publications or necessarily under review with a Lancet journal. These preprints are early-stage research papers that have not been peer-reviewed. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. The findings should not be used for clinical or public health decision-making or presented without highlighting these facts. For more information, please see the FAQs.
Perceptions of Portable Dentistry in Asia Using Machine Learning Models
15 Pages Posted: 6 Feb 2025
More...Abstract
Access to healthcare is a significant challenge for individuals with limited mobility, particularly in developing countries and among vulnerable populations in Asia. Portable dentistry has been proposed as an innovative solution to address these barriers, offering essential dental services at patients’ homes. This study explores the perceptions of patients and dentists regarding portable dentistry's feasibility and acceptability and employs machine learning models to predict its regional need and willingness. A cross-sectional, multi-country study was conducted over six months (June–November 2024) involving 1,550 patients and 1,320 dentists from Pakistan, India, Bangladesh, Iran, and Saudi Arabia. Data were collected using validated questionnaires distributed online and in person. Machine learning models—Random Forest, Gradient Boosting, K-Nearest Neighbors, Support Vector Machine, and Logistic Regression—were applied to predict the acceptance and regional needs of portable dentistry. The models were evaluated for accuracy, precision, recall, F1 score, and ROC-AUC metrics. Qualitative analysis was performed on open-ended responses to identify thematic insights. Approximately 76% of patients and 74% of dentists expressed a need for portable dentistry, with urban residents, women, and younger dentists showing the highest interest. Random Forest consistently achieved the highest performance in predictive analysis, with accuracies up to 97.10% for patients and 88.17% for dentists. Feature importance analysis revealed that demographic factors, professional experience, and perceived demand strongly influenced acceptance. Qualitative findings highlighted portable dentistry's potential benefits, including improved access, reduced travel, and personalized care, alongside challenges such as equipment limitations and operational constraints. The study demonstrates a substantial demand for portable dentistry in Asia, supported by robust predictive modeling and thematic analysis. Targeted awareness campaigns, professional training programs, telehealth integration, and economic viability assessments are recommended to facilitate the sustainable implementation of portable dentistry, addressing healthcare disparities and improving accessibility for underserved populations.
Keywords: deep learning, generative artificial intelligence, large language models, machine learning, modeling, natural language processing
Suggested Citation: Suggested Citation