Loading...
The Effect of Hypertension on Periapical Disease: a Retrospective Study Utilizing Machine Learning to Assess a Dental School Population
Asano, Jeffrey
Asano, Jeffrey
Citations
Altmetric:
Genre
Thesis/Dissertation
Date
2024-08
Advisor
Committee member
Group
Department
Oral Biology
Subject
Permanent link to this record
Collections
Research Projects
Organizational Units
Journal Issue
DOI
http://dx.doi.org/10.34944/dspace/10618
Abstract
Introduction: Hypertension is a chronic medical condition in which the blood pressure is elevated. Apical periodontitis and hypertension are both chronic conditions triggered by inflammatory processes that share similar molecular players. Previous studies have shown a relationship between cardiovascular disease and apical periodontitis, but few have used machine learning algorithms to process the data. Machine learning algorithms use artificial neural networks to form pattern recognition pathways and are highly customizable to any data-driven task. The purpose of this study was to predict the association between hypertension and periapical disease from the Temple Kornberg School of Dentistry electronic dental record using machine learning algorithms.
Materials and Methods: The integrated Axium data at the Temple Kornberg School of Dentistry was examined retrospectively after approval from the Temple University Institutional Review Board. The complete Health history data of patients who required primary endodontic therapy was collected, and periapical disease was defined as having symptomatic apical periodontitis, asymptomatic apical periodontitis, chronic apical abscess, or acute apical abscess. We identified large imbalances within the data, and so synthetic minority oversampling technique (SMOTE) was used for statistical analysis. After the application of SMOTE, XGB, random forest, Lasso, and SVM algorithms built separate models designed within R to predict periapical disease from the patients’ relevant information.
Results: The complete health history reports of 3888 patients who required primary endodontic therapy from January 2018 to December 2022 were collected. 1511 patients were diagnosed with some form of periapical disease. 610 Patients were diagnosed with hypertension. Among the four machine learning algorithms, XGB had the lowest brier score, highest accuracy and highest AUC ROC values. The mean brier score, accuracy, and AUC ROC was 0.137, 83.73%, and 88.59% respectively. XGB found “age”, “sex”, “high blood pressure”, and “Insurance – Medicaid” to be the most significant variables able to predict periapical disease.
Conclusion: While not statistically significant, hypertension was found to be one of the strongest health-related predictors of periapical disease. Surprisingly, we found non-health related variables such as insurance type, tobacco cessation, and insurance type to have a strong correlation with predicting periapical disease. With more patient data from Temple, it is possible to fine tune the algorithm to more accurately predict periapical disease based on each patient’s relevant health information.
Description
Citation
Citation to related work
Has part
ADA compliance
For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu