@article{VollmerVollmerLangetal.2022,
  author    = {Vollmer, Andreas and Vollmer, Michael and Lang, Gernot and Straub, Anton and Shavlokhova, Veronika and K{\"u}bler, Alexander and Gubik, Sebastian and Brands, Roman and Hartmann, Stefan and Saravi, Babak},
  title     = {Associations between periodontitis and COPD: An artificial intelligence-based analysis of NHANES III},
  series = {Journal of Clinical Medicine},
  volume    = {11},
  journal   = {Journal of Clinical Medicine},
  number    = {23},
  issn      = {2077-0383},
  doi       = {10.3390/jcm11237210},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312713},
  year      = {2022},
  abstract  = {A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70\% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953\% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results.},
  language  = {en}
}