Development of a survey-based stacked ensemble predictive model for autonomy preferences in patients with periodontal disease

Abstract

Objectives: This study aimed to develop a model to predict the autonomy preference (AP) and satisfaction after tooth extraction (STE) in patients with periodontal disease. Understanding of individual AP and STE is essential for improving patient satisfaction and promoting informed decision-making in periodontics.

Methods: A stacked ensemble machine learning model was used to predict patient AP and STE based on the results of a survey that included demographic information, oral health status, AP index, and STE. Data from 421 patients with periodontal disease were collected from two university dental hospitals and evaluated for ensemble modeling in the following predictive models: random forest, naïve Bayes, gradient boost, adaptive boost, and XGBoost.

Results: The models demonstrated good predictive performance, with XGBoost demonstrating the highest accuracy for both AP (0.78) and STE (0.80). The results showed that only 7.6 % of patients had high AP, which tended to decrease with age and varied significantly according to education level and severity of treatment, categorized as supportive periodontal treatment, active periodontal treatment, or extraction and/or dental implant procedures. Additionally, the majority of patients (67.7 %) reported high STE levels, highlighting the effectiveness of the model in accurately predicting AP, which was further supported by the significant correlation between accurately predicted AP levels and high STE outcomes.

Conclusions: The successful utilization of a stacked ensemble model to predict patient AP and STE demonstrates the potential of machine learning to improve patient-centered care in periodontics. Future research should extend to more diverse patient populations and clinical conditions to validate and refine the predictive abilities of such models in broader healthcare settings.

Clinical significance: The machine learning-based predictive model effectively enhances personalized decision-making and improves patient satisfaction in periodontal treatment.