# Classification of Alzheimer's Disease Patients Using Texture Analysis and Machine Learning
## 3 Results  
After obtaining the segmented hippocampus, 20 GLCM features were extracted with \(d=1\) and 2 for all the 13 possible directions, of which 8 (Autocorrelation, Energy, Entropy, Homogeneity, Maximum Probability, Correlation, and Information measure of correlations 1 and 2) were deemed to contribute the most to the classification by obtaining their feature score, and later analysis methods were shortlisted based on their GLCM feature score. After running GLCM on the dataset, 20 features were generated; however, the accuracy of the machine learning technique was relatively low. The lower accuracy was due to the dataset's more considerable variance; it had a few factors contributing to randomness; therefore, those specific attributes were removed. An F-type statistic test was employed, which analyses the variance of two dataset means to eliminate the implausible values. It provided the standard deviation value, which explained the variance between datasets, resulting in reduced accuracy. This finding of the F value of statistical attributes aided in eliminating features that were not viable. Age and structural data such as hippocampus volume were included in the dataset for comparison analysis, in addition to these 8 GLCM features. The research implementation platforms used python libraries and MATLAB. The classification model was trained using three different machine learning algorithms. SVM, Decision Trees, and Ensemble Bagged Trees were the models used (another name for Random Forest classifier). The dataset was taken into consideration when selecting these models. The classification was performed without setting advanced optimization techniques. Since the training dataset was limited (234), a classifier that can perform better with fewer data had to be chosen. The dataset with a clear separation between its classes performed well with SVM. Figure 10 gives the notion that for any machine learning algorithm, if the training and testing data was linearly separable, then the algorithms performed better than other nonlinear dataset classifiers. When instances of both the classes could be easily separated either by a straight line or nonlinearly, SVM became a suitable option for such data points. The scatter plot of several input features indicated that SVM would better fit the data set than other classifiers. SVM used a hard margin because the data was linearly separable. The regularization parameter (lambda) served as a degree of importance that was given to missclassifications. SVM posed a quadratic optimization problem that looks for maximizing the margin between both classes and minimizing the amount of missclassifications. The reason for choosing SVM, Decision Trees, and random forests is because of their implementation simplicity and relatively better model accuracy. Logistic regression, artificial neural networks, and nonlinear SVM work better when the data is nonlinear [28; 29; 30]. The number of trees in the Random Forest depends on the number of rows in the data set. In the case of a random forest, hyper parameters include the number of decision trees in the forest and the number of features considered by each tree when splitting a node. (The parameters of a random forest are the variables and thresholds used to split each node learned during training) [31]. Decision trees, which are also supervised learning algorithms, are another algorithm utilized in this section. The key reason for utilizing this technique is that it eliminates the requirement for the normalization of datasets. The dataset used has different scales for some attributes, therefore using this technique eliminates normalization. Random Forest, also known as Ensemble Bagged Tree, is the third and final algorithm. It is a Decision Trees extension. It selects the best model from a set of uncorrelated decision tree classifiers. It takes longer to train than the other two algorithms, but it produces higher accuracy than the Decision Trees algorithm.