# DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
## 5 Results and Discussion
### Evaluation Results of Our Models  
After proper feature selection and training of our ML methods, performance (accuracy, F1-score, precision, and recall) is essential in selecting the appropriate ML for our prediction model. This work performs a binary classification on the presence or absence of dental caries. Table 2 shows the performance results of all the ML methods on the Test set. We observed that the RF outperformed the other applied ML methods and any neural network-based methods. Our proposed approach using RF displayed an accuracy, F1-score, precision, and recall of 0.92, 0.90, 0.94 and 0.87, respectively. Therefore, the presented results of RF can be seen as highly competitive compared to other prediction methods applied.  
It is essential to mention the performances observed when DL was applied during our proposed paper. Three DL algorithms were analyzed: ANN, CNN, and LSTM, out of which the ANN outperformed the other DL methods. The results of the different DNNs are shown in Figure 3. We performed our experiments with the tuned parameters learning rate of 0.001, epochs of 200 for ANN and CNN, and 100 for LSTM and drop out of 0.20.  
Figure 4 presents a graphical representation of accuracy, F1-score, precision, and recall ranking prediction results of all the ML methods used. As a result of analyzing the performance of the ML methods on our dataset, RF showed the highest performance among the ML used, followed by ANN, CNN, GBDT, SVM, LR, and then LSTM. When compared to each other, RF accuracy was better than ANN by 4%, CNN by 5%, GBDT by 7%, SVM by 9%, LR by 10%, and then LSTM by 17%. Similarly, F1-score, precision, and recall of RF also outperformed all other ML used. Regardless of the high-end result achieved by the ANN among the DNN algorithms used on the test set, RF still stands out to be the best algorithm for our proposed paper.  
During our experiments, the dataset was divided into training and test sets. The training set was further divided into a training and validation set. \(K\)-fold cross-validation was used during our grid search hyperparameter tuning step on the validation set to tune the hyperparameters, while models were trained on the training set. In our case, we used \(K=10\), in which during each fold, training and validation were partitioned into a \(K\) subset for validation and \(K\)-1 subset set for training. The test set was left aside to evaluate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline RF & 0.92 & 0.91 & 0.94 & 0.88 \\ ANN & 0.88 & 0.87 & 0.87 & 0.87 \\ CNN & 0.87 & 0.87 & 0.87 & 0.87 \\ GBDT & 0.85 & 0.81 & 0.83 & 0.78 \\ SVM & 0.83 & 0.79 & 0.82 & 0.76 \\ LR & 0.82 & 0.78 & 0.80 & 0.76 \\ LSTM & 0.75 & 0.74 & 0.74 & 0.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of different ML methods used.  
the performances of our different ML methods. These results revealed that our models performed well on unseen data as they did on the training Set.  
Figure 3: Accuracy vs. loss performance evaluation of different neural network models used. (**a**) Training vs. validation accuracy ANN. (**b**) Training vs. validation loss ANN. (**c**) Training vs. validation accuracy CNN. (**d**) Training vs. validation loss CNN. (**e**) Training vs. validation accuracy LSTM. (**f**) Training vs. validation loss LSTM.