Identification of Risk Group for Root Caries and Analysis of Associated Factors in Older Adults Using Unsupervised Machine Learning Clustering - Jiang L; Huang S; Reissmann DR; Schmalz G; Li J (Clinical Interventions in Aging)
Linxin Jiang,1,2 Shaohong Huang,1 Daniel R Reissmann,2 Gerhard Schmalz,3 Jianbo Li1 1Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China; 2Department of Prosthodontics, University of Leipzig, Leipzig, Germany; 3Department of Conservative Dentistry and Periodontology, Brandenburg Medical School Theodor Fontane (MHB), Brandenburg/Havel, GermanyCorrespondence: Jianbo Li, Stomatological Hospital, School of Stomatology, Southern Medical University, 366 Jiangnan South Avenue, Haizhu District, Guangzhou, Guangdong province, 510280, People’s Republic of China, Email 435264691@qq.com Gerhard Schmalz, Email Gerhard.Schmalz@mhb-fontane.dePurpose: This study aimed to identify the high-risk group for root caries using unsupervised machine learning and to explore the associated factors.Patients and Methods: This cross-sectional study included 423 adults aged 65 to 74 years, surveyed in 2021. Clusters representing root caries risk were identified using k-prototypes clustering, with the optimal number of clusters determined by the maximum silhouette index. The confusion matrix and alluvial diagram were used to visualize the predictive accuracy and composition of the clustering results. Binary logistic regression models further analyzed the associated factors, while ROC (receiver operating characteristic) curves and the random forest model visualized the predictive performance and the most important associated factors.Results: Two clusters were identified: cluster 1, with low root caries risk (21.5% with and 78.5% without root caries), and cluster 2, with high root caries risk (83.7% with and 16.3% without root caries). The clustering results predicted root caries with an accuracy of 0.81, sensitivity of 0.79, and specificity of 0.83. Overlapping results from binary logistic regression and the random forest model indicated that older age, more periodontal pockets, more attachment loss, female, a history of systemic diseases, presence of xerostomia, and presence of unrestored tooth loss were positively associated with cluster 2. Brushing tooth ≥ 2 times per day and a high level of oral health knowledge were negatively associated with cluster 2. The ROC curve for the binary logistic regression model showed an AUC (area under the curve) of 0.84.Conclusion: Individuals who are older, female, with poorer oral and systemic health status, suboptimal oral hygiene behaviors, and lower oral health knowledge levels are more likely to be identified as high-risk group. The identified factors, revealed through unsupervised machine learning, can facilitate personalized prevention and management strategies for root caries in older adults.Keywords: older adults, oral health, risk analysis, machine learning