# Classification of Alzheimer's Disease Patients Using Texture Analysis and Machine Learning
## 3 Results
As per the data points generated from our dataset, Decision Trees, SVM, and Random Forest classifiers were chosen as they are simpler and more accurate as compared to other machine learning classifiers [32]. The dataset has linear boundaries and hence was better suited for the selected classifiers. The confusion Matrix for the SVM, Decision Trees and Ensemble are illustrated in Figure 11.  
Figure 10: Scatter–plot representation of the GLCM features (AD-Blue, CN-Orange): (**a**) Age vs. Hippocamp Volume; (**b**) Autocorrelation vs. Correlation; (**c**) Entropy vs. Energy; (**d**) Homogeneity vs. Maximum Probability; (**e**) Information measures of correlation 1 and 2. The accuracy of the Decision Trees, SVM, and Ensemble models are shown in Table 4. Only the volume and age were used as input features in these models. SVM outperformed the other two models in terms of accuracy. After observing the scatter plot of age and volume in Figure 10a, volume was the factor due to which SVM had superior accuracy. Figure 12 shows the Receiver Operating Characteristics (ROC) curve for SVM, Decision Trees, and Ensemble techniques. From Figure 10b, the autocorrelation for AD patients was lower than compared to CN subjects. The uniformity in volumes of AD patients was higher than that of CN subjects, and the difference between neighbouring pixels was lower than that of CN subjects, as can be observed from Figure 10c. The randomness of intensity distribution was high for CN and was low for AD subjects. AD subjects had slightly more linear structures compared to CN. Figure 13 shows the comparative analysis of the volume data between AD and CN groups and it is seen that there is a noticeable distinction among the data samples. In the next phase, classification models were trained only on the GLCM characteristics that had been identified. For textural features, Ensemble provided the highest accuracy (85%) at \(d=1\) and direction (\(d\), \(d\), 0), while Decision Trees was least accurate at \(d=2\) in direction (\(d\), \(d\), \(d\)). One evident finding from the accuracy table is that as the GLCM distance parameter was increased, the model's accuracy decreased since average accuracy dropped from 82.60% to 82.23% for textural features. The right and left hippocampus were exceptionally small in the MRI, and as the distance parameter was increased, the quality of the GLCM matrix decreased due to its small dimensions; hence the distance was limited to \(d=2\). While the model was trained with eight GLCM features, i.e., Textural data, the results produced were unsatisfactory as it had less accuracy than the results produced with Textural and Structural Data. Table 5 represents the classification accuracy with Textural and structural data as input. The texture features were merged with volume and age data. While the model was trained just on GLCM features, accuracy ranged from 79.1% to 85%. When only volume data was provided, the maximum accuracy was 87.2%. When the GLCM features dataset was paired with volume data, including the patient's age and hippocampal volume, the accuracy improved up to 90.2%. The Ensemble Bagged Trees algorithm performed the best with the highest accuracy; one factor for better performance was the additional variables provided for the dataset. By including the structural data, a 4.38% improvement was observed on average. The combination of features produce improvement by 4.38% on the structural data. Hence, it was observed as a better fit for the dataset. Figure 14 shows average accuracy for top performing machine learning algorithms applied on the data set. Table 6 summarizes the classification accuracy based on texture analysis and machine learning techniques. Figure 13: Comparison of volume data between AD and CN groups. Figure 14: Average accuracy for top-performing machine learning algorithms.