# DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
## 5 Results and Discussion  
### Evaluation Metrics  
To evaluate the performance of our prediction models and avoid overfitting, our dataset was split into training (80% of data samples) and test (20% of data samples) sets. The training set was further divided into training (80% of the Training set) and validation (20% training set) sets. This work compared the commonly used evaluation metric (accuracy, precision, recall, and F1-score) of the machine learning experimented on RF, SVM, GBDT, LR, ANN, LSTM, and CNN. The mathematical equations of our evaluation metrics are defined below:  
\[Precision=\frac{TP}{TP+FP} \tag{14}\]  
\[Recall=\frac{TP}{TP+FN} \tag{15}\]  
\[F1-Score=2\times\frac{(Precision\times Recall)}{(Precision+Recall)} \tag{16}\]  
\[Accuracy=\frac{TP+FP}{(TN+TP+FN+FP)} \tag{17}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Machine Learning** & **Parameters** & **Range** & **Description** \\ \hline RF & n\_estimator & 160 & Number of decision trees to use in the forest \\  & max\_depth & None & Maximum depth of each tree \\  & min\_samples\_leaf & 1 & Number of samples required at the leaf node \\  & max\_features & auto & Number of features to consider for each tree \\ \hline GBDT & n\_estimator & 100 & \\  & max\_depth & None & Identical to the above \\  & min\_samples\_leaf & 2 & \\  & max\_feature & Auto & \\  & subsample & 0.85 & Controls the percentage of observations; sed to create \\  & & & a score for each tree \\ \hline SVM & kernel & rbf & Kernel type to be used by the algorithm \\  & gamma & 0.0001 & Kernel factor in controlling the distance of influence of \\  & & & a training point \\  & & \(C\) & 1000 & Adds a penalty for each misclassified data \\  & probability & True & The parameter that enables probability estimate \\ \hline  & penalty & \(l_{1}\) & Specifies the norm of penalty (none, \(l_{1}l_{2}\) or elastic net) \\  & & \(C\) & \(1\) & Specifies weight importance for training data or \\  & & & complexity penalty \\  & solver & liblinear & Specifies the algorithm used in the optimization \\  & & & problem \\ \hline  & learning rate & 0.001 & Determines the step size at each iteration while \\  & & & minimizing the loss. \\  & epoch & 200 (ANN, CNN) & Specifies the number of passes on the entire training \\ ANN & & 100 (LSTM) & dataset that the ML performs. \\ CNN & & 1000 (ANN) & Indicates the number of training samples used during \\ LSTM & batch size & 1380 (CNN) & one epoch. \\  & & 480 (LSTM) & \\  & & 30 (ANN) & \\  & patience & 10 (CNN) & Specifies early stop if no progress on the validation set \\  & & 50 (LSTM & \\ \hline \end{tabular}
\end{table}
Table 1: Optimizable parameters for different models.  
PrecisionEquation (14) is defined as the ability of a classifier to identify the relevant data points. TP (true positive) is when a positive observation is also predicted as positive. FP (false positive) is a negative observation that is indicated as being positive. RecallEquation (15) is the measurement that defines how correctly a model has identified the positive classes in our dataset classes. FN (false negative) represents a positive observation that was predicted as being negative. F1-ScoreEquation (16) is the measurement of the model's accuracy on the dataset. The weighted average between the precision and recall are defined in Equations (14) and (15). AccuracyEquation (17) is the measurement that determines a model's efficiency at identifying patterns and relationships between features in the datasets, which is the ratio between the correctly predicted value by the total number of assessments, where TN (true negative) is the negative observation correctly predicted as negative.