# Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm
## 3 Materials and Methods
### Methodology
#### 3.2.2 Classification
The proposed work provided a high recognition percentage of 95.71% compared to previous research work, which achieved lower recognition accuracies 76.92%, 79.77%, 74.05%, 92.5%, 79%, 95.55%, and 93.3%. Moreover, it should be declared that the number of extracted features in the current study was lower than that found in the literature, which significantly achieved lower computational time in classifying the liver. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Decision Tree (J48)**} \\ \hline
**Class** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline Fatty & 0.944 & 0.091 & 0.944 & 0.944 & 0.944 & 0.927 \\ Normal & 0.909 & 0.056 & 0.909 & 0.909 & 0.909 & 0.927 \\ AVG & 0.931 & 0.077 & 0.931 & 0.931 & 0.931 & 0.927 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Precision, Recall, and F1-Measure for J48 algorithm. Figure 8: Precision, Recall, and F1-Measure for J48 algorithm. Figure 7: Ten-fold cross-validation method. The limitation of our technique is a deficiency of available databases. Moreover, the low quality of acquisition ultrasound devices leads to a requirement for improved methods including more efficient functionality without degrading the ultrasound image content. As future research, the accuracy of recognition of the proposed method could be improved by reducing the rate of False-Negative (FN) cases to allow us to better distinguish between closely resembling diseases. Future work would be on increasing the size of patient datasets to improve and increase the classification accuracy of the system and generalizing the proposed method to deal with US images. Class imbalance is also challenging. For instance, liver cirrhosis is usually so limited compared to other classes; e.g., liver steatosis, liver cancer, and liver fibrosis. Additionally, more specific classes can be added, e.g., stages of fibrosis; F1, F2, F3, and F4. It is should be mentioned that these classes of fibrosis are so difficult to be recognized by specialists radiologist. This is because some details are invisible to human eyes. Before CAD technology and artificial intelligence are incorporated into routine clinical practice, several challenges must be overcome. For clinical use, if there are concerns about unreliable predictions, a CAD system must be able to justify its analysis. The possibility that CAD will replace completely human decision making is unlikely in the meantime. Should CAD technology be used as the strategy of resect-and-discard for benign tumors then photo-documentation with high-resolution images marked with CAD decisions will be needed for medical-legal reasons. Conceptualization, A.G., H.A.Y., A.M.H. and A.H.; methodology, A.G., A.H., A.M.H. and H.A.Y. ; software, A.G. and A.H.; validation, A.G., A.H., H.A.Y. and H.M.A. ; investigation, A.G., A.H. and H.A.Y. ; resources, A.G.; data curation, A.G., A.M.H. and H.A.Y. ; writing--original draft preparation, A.G., A.H.; writing--review and editing, A.G., A.M.H. and H.A.Y. ; supervision, A.H. and H.A.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data sharing is not applicable. The authors are grateful to Elroowad Radiography Center (Menouf City, Egypt), especially to [PERSON] for providing the USA images used in this investigation, and for supportive discussions. The authors declare no conflict of interest. ## References

* _[PERSON] et al. (2018)_ [PERSON]; [PERSON] Liver diseases: A major, neglected global public health problem requiring urgent actions and large-scale screening. _Liver Int._**2018**, _38_, 2-6. [CrossRef] [PubMed]
* _[PERSON] et al. (2020)_ [PERSON]; [PERSON] Nonalcoholic fatty liver disease 2020: The state of the disease. _Gastroenterology_**2020**, _158_, 1851-1864. [CrossRef] [PubMed]
* _[PERSON] et al.