# Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm
## 3 Materials and Methods
### Methodology
#### 3.2.2 Classification
### Machine Learning Algorithms

Supervised learning algorithms were employed in this investigation to classify the liver tissues into fatty and normal using the same data set that was used before in the genetic algorithm. Several algorithms were applied to construct the classifier model, but we reported the greatest classifier decision tree (J48), that given the high accuracy, it achieved accuracy reaching up to 93.12%, according to the main parameters, the Kappa statistic was 0.85, mean absolute error was 0.069, root mean squared error was 0.26, relative absolute error was 0.1451, and root relative squared error was 0.5383. This classifier was tested using the cross-validation (10 folds) method; in this method, the dataset was divided randomly into 10 folds, each fold was held out once to test the classifier, and the classifier was trained on the remaining (10-1) to build the classifier as shown in Figure 7[44]. Each fold represented one iteration, in each iteration, the accuracy of the model was calculated, and the overall accuracy was calculated using the mean; this method was the best to evaluate the performance of the model, based on different measures mainly: TP rate, FP rate, Precision, recall, and F1-measure as shown in Table 7 and also presented in Figure 8. The Experimental Results were developed and evaluated using WEKA software version 3.6.11. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Classes (No. of Patients)** & **Features/Classifier** & **Performance** \\ \hline \multirow{3}{*}{[PERSON] et al. [23]} & \multirow{3}{*}{42 Natural, 58 Fatty} & Texture, wavelet transform, and DT/HOS and FSC & An accuracy: 93.3\% \\ \hline \multirow{3}{*}{[PERSON] et al. [18]} & Natural, Fatty. 177 & GLRLM, law’s texture energy, FOS, GLCM, and fractal dimension/ANN, SVM\&KNN & 76.92\% \\  & & & & 79.77\% \\  & & & & 74.05\% respectively \\ \hline \multirow{3}{*}{[PERSON] et al. [26]} & Natural (30), Fatty (10), Cirrhotic (10), Hep. (10) & GLRLM, Invariant & \multirow{3}{*}{92.5\%} \\  & & & & \\ \cline{1-1} \cline{5-5}  & & & & histogram, GLCM/BPNN \\ \hline \multirow{3}{*}{[PERSON] et al. [50]} & \multirow{3}{*}{Natural (68), Fatty (52)} & FOS, GLCM, GLRLM, Gabor filter, Laws’ filter, lacunarity, fractal dimension, hepatoreal coefficient, attenuation/ ANN, SVM, k-NN, Bayes, DT & \multirow{3}{*}{classifiers fusion:79\%} \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [16]} & \multirow{3}{*}{45 Natural, 45 Fatty} & FOS, GLCM, GLRLM, Law’s TEM, FPS, Fractal & \multirow{3}{*}{Accuracy: 95.55\%, Fractal} \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \hline \multirow{3}{*}{The current work} & Natural (155), Fatty (145) & 26 features/Voting Function & \multirow{3}{*}{Accuracy: 95.71\%} \\ \cline{1-1} \cline{5-5}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the suggested method and other systems. ## 5 Conclusions

Early diagnosis of FID is important to avoid advanced stages. Due to its low cost and availability, radiologists commonly use US imaging. However, it has problems in detecting this disease due to its low quality. The current study provided a technique to accurately distinguish between fatty /normal livers using ultrasound liver images. Ultrasound, as a noninvasive method, was known for diagnosing diffused liver diseases. In this work, multi ROIs were selected to reduce the required computations. From each ROI, 26 features were extracted. The acquired image of fatty tissue was characterized by bright echogenicity with high contrast and high variance. All thresholds and parameters were automatically selected via GA. The final result depended on the number of normal/fatty ROIs. The obtained TP was 94.28%, TN was 97.14%, FP was 5.71%, and FN was 2.85%. The achieved precision was 94.28%, sensitivity was 97.05%, specificity was 94.44%, and F1-score was 95.64%.