# A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography +
## 4 Discussion and Conclusions  
The automatic classification of patients based on breast density can represent an important result for carrying out targeted screening programs based on early diagnosis. The study aimed to evaluate the possibility of classifying the breast density on a small dataset of patients, who were undergoing mammography screening programs, thanks to the extraction of textural features from images. The classification of breast density through CAD systems based on textural features extracted from the image has already been investigated in the recent scientific literature. [PERSON] and [PERSON] [50] made a binary classification based on textural features obtained through Laws' texture energy, applying principal component analysis (PCA) to reduce the dimensionality of feature vectors and KNN and NN classifiers, obtaining a maximum accuracy of 95%. The NN classifier was also used by [PERSON] et al. [51], using textural features based on histograms and Haralick texture descriptors. In this case, a maximum accuracy of 92.9% was obtained when considering only Haralick texture descriptors, which reached 98.95% when histogram features were also considered. A multiclass classification was also made in [52], which resorted to a fuzzy classification based on textural features extracted through the co-occurrence matrix, reaching an accuracy of 84.2%. In all these cases, the dataset imbalance problem was not addressed. Our results showed an agreement with the radiologist's report depending on the cases: for a binary classification aimed only at the distinction between dense and non-dense breasts, the highest accuracy of 93.55% was obtained with an SVM method, with k = 0.8675, and percentage of true positives and negative equal to TPP = 94.44% and TNP = 92.31%. For multiclass classification, the best accuracy in terms of OCA (Overall Classifier Accuracy) was equal to 82.14%, with k = 0.7089, obtained with a two-layer neural network. This value is considered encouraging compared with the results obtained from the application of machine learning methods for multiclass classification because we followed a deep learning approach. In this case, the maximum accuracy achieved was equal to 60%, with k = 0.3583, a value that has not shown improvements with the application of oversampling techniques, which improved the estimate on the minus class significantly, reaching a maximum overall accuracy of 63.33% with k = 0.4931. The application of neural networks provided the most encouraging results for acting on unbalanced datasets for multiclass classification, despite the low number of patients. Conceptualization, F.A. ([PERSON]) and M.S. ; methodology, A.M.P., F.A. ([PERSON]), M.S. and F.A. ([PERSON]); validation, A.M.P., F.A. ([PERSON]), M.S., F.A. ([PERSON]) and R.G. ; formal analysis, A.M.P. and F.A. ([PERSON] \begin{table}
\begin{tabular}{l c c} \hline _Overall Statistics_ & _Accuracy (95\% CI)_ & _Kappa_ \\ \hline
2 layers/500 nodes & 0.82 (0.63–0.93) & 0.71 \\ \hline
2 layers/250 nodes & 0.71 (0.51–0.97) & 0.50 \\ \hline \end{tabular}
\end{table} 
Table 4: Overall accuracies of two dense layers networks with different numbers of nodes. \begin{table}
\begin{tabular}{l l l l l} \hline _Statistics_ & _Class_ & _Specificity_ & _Sensitivity_ & _Balanced Accuracy_ \\ \hline \multirow{4}{*}{500 nodes} & A & 0.33 & 1.00 & 0.67 \\ \cline{2-5}  & B & 0.93 & 0.84 & 0.89 \\ \cline{2-5}  & C & 0.50 & 0.96 & 0.73 \\ \cline{2-5}  & D & 1.00 & 0.91 & 0.95 \\ \hline \multirow{4}{*}{250 nodes} & A & 0.33 & 0.96 & 0.65 \\ \cline{2-5}  & B & 0.93 & 0.61 & 0.77 \\ \cline{1-1} \cline{2-5}  & C & 0.25 & 1.00 & 0.62 \\ \cline{1-1} \cline{2-5}  & D & 0.67 & 0.91 & 0.79 \\ \hline \end{tabular}
\end{table} 
Table 5: Evaluations metrics of the two methods for the different classes. [PERSON]); investigation, A.M.P., F.A. ([PERSON]) and C.R. ; data curation, A.M.P., F.A. ([PERSON]), M.P.B. and G.G. ; writing--original draft preparation, A.M.P., F.A. ([PERSON]), M.S. and F.A. ([PERSON]); writing--review and editing, A.M.P., F.A.