Developing and testing a prediction model for periodontal disease using machine learning and big electronic dental record data

Abstract

Despite advances in periodontal disease (PD) research and periodontal treatments, 42% of the US population suffer from periodontitis. PD can be prevented if high-risk patients are identified early to provide preventive care. Prediction models can help assess risk for PD before initiation and progression; nevertheless, utilization of existing PD prediction models is seldom because of their suboptimal performance. This study aims to develop and test the PD prediction model using machine learning (ML) and electronic dental record (EDR) data that could provide large sample sizes and up-to-date information. A cohort of 27,138 dental patients and grouped PD diagnoses into: healthy control, mild PD, and severe PD was generated. The ML model (XGBoost) was trained (80% training data) and tested (20% testing data) with a total of 74 features extracted from the EDR. We used a five-fold cross-validation strategy to identify the optimal hyperparameters of the model for this one-vs.-all multi-class classification task. Our prediction model differentiated healthy patients vs. mild PD cases and mild PD vs. severe PD cases with an average area under the curve of 0.72. New associations and features compared to existing models were identified that include patient-level factors such as patient anxiety, chewing problems, speaking trouble, teeth grinding, alcohol consumption, injury to teeth, presence of removable partial dentures, self-image, recreational drugs (Heroin and Marijuana), medications affecting periodontium, and medical conditions such as osteoporosis, cancer, neurological conditions, infectious diseases, endocrine conditions, cardiovascular diseases, and gastroenterology conditions. This pilot study demonstrated promising results in predicting the risk of PD using ML and EDR data. The model may provide new information to the clinicians about the PD risks and the factors responsible for the disease progression to take preventive approaches. Further studies are warned to evaluate the prediction model's performance on the external dataset and determine its usability in clinical settings.

Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease

“…Table 1 provides a summary of the related research and refers to previous studies on predictions involving metabolic syndrome and/or periodontal disease, using ML models where they appear as the target variable or feature variable. The study presented in [45] focuses on the development and testing of a prediction model for periodontal disease. Leveraging machine learning techniques and extensive electronic dental record data, the authors aimed to enhance accuracy in predicting periodontal disease.…”

“…To increase the transparency and acceptance of our ML models and to strengthen confidence in the results obtained, we made use of permutation explainer implemented in the SHAP framework. The same framework was used to explain the predictions in papers [45,47,48,52].…”

Identifying predictors of tooth loss using a rule‐based machine learning approach: A retrospective study at university‐setting clinics

“…In recent years, machine learning algorithms and deep learning models have been widely used to build diagnosis and prediction tools for dental diseases 13–18 . However, deep learning models are complicated black box models that usually fall short of interpretability.…”

“…In recent years, machine learning algorithms and deep learning models have been widely used to build diagnosis and prediction tools for dental diseases. [13][14][15][16][17][18] However, deep learning models are complicated black box models that usually fall short of interpretability. Machine learning models, especially tree-based methods, provide great potential for accurate prediction while preserving model interpretability.…”

Periodontitis and Metabolic Syndrome: Statistical and Machine Learning Analytics of a Nationwide Study

“…Through ML, it is possible to exploit multiple variables using easy and rapid extraction, such as the clinical history of the patients as well as anthropometric or demographic characteristics, to facilitate the identification of otherwise complex pathologies [23]. In dentistry, several studies applied ML for periodontitis classification, producing encouraging results [24][25][26][27][28]. Limitations of these studies include using data from a single institution [25], not consistently including social determinants of health and systemic conditions or relying on patient-reported data, and not involving EMRs.…”