# DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
## 5 Results and Discussion
### Discussion  
The usage of ML in oral healthcare has shown its success in increasing the oral health condition of individuals by providing dental professionals with a tool, giving them an ability to make early decisions to prevent dental caries to individuals, hence improving their overall lifestyle condition.  
This paper has explored and investigated several ML methods (SVM, LR, GBDT, RF, and DL) on our dental caries dataset and identified RF as the model displaying the highest performance. The proposed model highly predicted dental caries with an accuracy of 92% while using RF as a classification algorithm. When feature calibration was performed to increase the model performance using feature selection techniques such as ANOVA and RF, all ML methods witnessed a drop in their performances except RF. We performed experiments on various DL (CNN and LSTM) regardless of their usage specificity (CNN) appropriate for the images dataset and LSTM for the time series dataset due to the history between the training sample) on the specific type of dataset. Nonetheless, these experiments were conducted for experimentation purposes, an observation of how the model would perform on such data.  
A similar study on dental caries prediction by [38] had an accuracy of 97%; this study found some existing correlation between parameters such as age, income, date of last dental visit, hours of television watching, and the presence of caries in the studied population. According to their study, a person's age was one of the essential predictive values because of the exposure of the root surface of aged people. One of the barriers to dental care access was identified as to be the low income of some families, given they were unable to obtain proper and regular health care checkups. Parameters such as oral health variables, lifestyle, and demographics were essential features to their oral health status. In the current study, such parameters were not considered due to our dataset's lack of such information. Similarly, age was not considered an essential feature during our study and was removed. All patients in our study were of the same age (12 years old). Therefore, other specific parameters such as infectious tooth parameters collected by experts were used for our prediction. Some of the most critical features playing an essential role in predicting dental caries were captured by our ANOVA F test feature selection, as shown in our source file of feature selection on our repository. We demonstrated that using the complete set of features in this study was more effective than reducing the dimensionality of our dataset. In this investigation, ML methods helped us identify the probable cause (attribute features) of dental caries presence in patients and thus could help in the implementation of a recommendation and decision support system to facilitate diagnostic, early detection, and recommendation to future patients, given the rarity of oral health decision support system based on ML. This paper proposes a solution that would significantly reduce the time, cost, and human power required to perform a similar job in current dental healthcare.  
Figure 4: Graphical representation of machine learning model metric.