# Modified Gingival Index (MGI) Classification Using Dental Selfies
## 3 Results  
We randomly divided our 44 participants into two groups--39 for training and five for testing, resulting in a training dataset of 1336 tooth images and a test dataset of 184 tooth images. As described in the method section, just 35 features were extracted from each image. We utilized the H2O AutoML package with its default settings, to guarantee reproducibility, we set the max_model (the number of algorithms to be tested) to 30, and the seed to 1 (Source code for h2o.automl.autoh2o). Next, we will report results, for each of the two stages of the cascade. First, training results will be presented: a table reporting the accuracy by the Average AUC (Area Under the Curve) score, achieved by the best performing classifier for each tooth-position in 5-fold cross-validation, and a histogram of the features selected--by contribution to accuracy of these classifiers. Then, test-set results will be presented in a table reporting the accuracy (AUC score) achieved by each tooth-position classifier-features combination. Looking at Table 2, which sets out the statistics for the best-performing classifier per tooth position, you can see that XGBoost was found to be the most accurate classifier for six of the eight positions. Regarding feature selection, the five features found to have the greatest contribution to successful classification during training were selected. Figure 9 presents the five features with greatest contribution, for the eight classifiers--one per tooth-position. K-mean var was found to be among the most contributing features for six of eight tooth positions. TN (IM)--the specialized dental-image feature we developed--was among the most contributing for five of eight positions. HistMatch (SD) had the third most positions and it was among the most contributing for four of eight. Now, let us turn to the test-set results. Table 3 presents the AUC score results. It is noteworthy that although the test-set is quite small (about 20 images per tooth-position) the AUC score obtained ranges from 1.0 for positions #12 and #42 to 0.69, with just two positions scoring below 0.8. Additionally, note that the accuracy of our method is generally higher for the lateral incisors (position #s ending with 2) than the central incisors (those ending with 1), and higher for the lower teeth (position #s beginning with 3 and 4) than the upper teeth. Additional information regarding the performance of the first stage test-set, including F1 at threshold scores and confusion matrices, can be found in Table A1 and Figure A1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tooth Number** & **Classifier** & **AUC Score** \\ \hline
12 & XGBoost & 0.99 \\
11 & XGBoost & 0.92 \\
21 & MLP & 0.76 \\
22 & XGBoost & 0.85 \\
42 & XGBoost & 0.98 \\
41 & XGBoost & 0.88 \\
31 & GBM & 0.72 \\
32 & XGBoost & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average AUC (Area Under the Curve) results of the best performing classifier, in the first stage of the cascade, best classifier per tooth-position selected during the training step in 5-fold cross-validation. At the second stage of the cascade, another binary classifier classifies the degree of inflammation as mild or severe, based on the MGI score. Here, again, we will first report the 5-fold cross-validation results from training for the best performing classifiers and selected--most-contributing--features (Table 4), then we will show the test-set results for the classifier-features combinations (Table 5). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tooth Number** & **Classifier** & **AUC Score** \\ \hline
12 & GBM & 0.92 \\
11 & XGBoost & 0.93 \\
21 & MLP & 0.95 \\
22 & XGBoost & 0.87 \\
42 & XRT & 0.98 \\
41 & XGBoost & 0.88 \\
31 & MLP & 0.72 \\
32 & MLP & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average AUC results of the best performing classifier, in the second stage of the cascade, best classifier per tooth-position selected during the training step in 5-fold cross-validation.