Affiliations: [a] University of Wisconsin-Milwaukee, Milwaukee, WI, USA | [b] Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, WI, USA
Correspondence:
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Corresponding author: Neel Shimpi, Associate Research Scientist, Center for Oral and Systemic Health, Marshfield Clinic Research Institute, 1000 North Oak Avenue, Marshfield, WI 54449, USA. Tel.: +1 715 221 6430; Fax: +1 715 221 6402; E-mail: shimpi.neel@marshfieldresearch.org.
Abstract: BACKGROUND: Periodontitis (PD), a form of gum disease, is a major public health concern as it is globally prevalent and harms both individual quality of life and economic productivity. Global cost in lost productivity is estimated at US$54 billion annually. Moreover, current PD assessment applies only after the damage has already occurred. OBJECTIVE: This study proposes and tests a new PD risk assessment model applicable at point-of-care, using supervised machine learning methods. METHODS: We compare the performance of five algorithms using retrospective clinical data: Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree (DT). RESULTS: DT and ANN demonstrated higher accuracy in classifying the patients with high or low PD risk as compared to NB, LR and SVM. The resultant model with DT showed a sensitivity of 87.08% (95% CI 84.12% to 89.76%) and specificity of 93.5% (95% CI 91% to 95.49%). CONCLUSIONS: A predictive model with high sensitivity and specificity to stratify individuals into low and high PD risk tiers was developed. Validation in other populations will inform translational value of this approach and its potential applicability as clinical decision support tool.
Keywords: Data mining, decision support systems clinical, health information systems, periodontitis, electronic health records, risk assessment tools
