# Prediction of Glioma Grades Using Deep Learning with Wavelet Radionic Features
## 3 Materials and Methods
### Statistical Analysis and Feature Selection  
The fact that there are too many features in a study extends the calculation time while the absence of a semantic relationship between the features reduces the classification accuracy. For this reason, feature selection has been applied. There are many methods such as Principal Component Analysis (PCA) and sequential forward selection (SFS) analysis to extract and select the most suitable feature set. The PCA method is a mathematical data mining method that facilitates the determination of the components of the data carrying the most information and reduces the data in this way. The PCA method is used to determine the most fundamental factor based on the relationships between variables, but the purpose of our study is to determine the variables that differentiate Grade II and GradeIII groups from each other. Therefore, it is important that 126 variables out of 744 variables reveal significant differences between Grade II and Grade III. In the study, while extracting the radiomic feature, it is not aimed to define the properties in a single variable. Therefore, Statistical analysis of data on radiomic features was done with the Statistical Package for the Social Science (SPSS) statistical computer program [50]. Texture feature extraction is based on statistical distributions. We analyzed first-order features (Global) and texture features (GLCM, GLDM, GLRLM, GLSZM, and NGTDM). It is useful software that is widely preferred in medical studies as well as social sciences. We performed univariate analysis based on the Mann-Whitney U test of significance for comparisons between grades and multiscale texture types (i.e., LLL, HLL, LHL, HHL, LLH, HLH, LHH, and HHH). Since the data of 121 patients with Grade II (\(n\) = 77) and Grade III (\(n\) = 44) were not normally distributed. Whether there are missing values and outlier values is analyzed with SPSS program. \"Kurtosis and Skewness\" values are checked to analyze whether the data have a normal distribution or not [51]. According to the literature, it is stated that these values have a normal distribution if they are in the range of \(\pm\)3 or \(\pm\)2 [52]. Accordingly, it was seen that the data did not have normal distribution, missing value and outlier. In line with these results the statistical significance of the data was tested with the non-parametric test, Mann-Whitney U. We detected the radiomic features that created significant differences between Grade II and Grade III. Spearman correlation analysis was used because variables were obtained by proportional or intermittent scales, but did not conform to normal distribution. The \(p\)-value indicates the amount of possible error that we will have when we encounter a statistically significant difference in a comparison. According to the HolmBonferroni method, a correlation value of 0.3 to 0.4 means a medium correlation and \(p<0.05\) is considered statistically significant. A total of 126 radiomic features with values of \(p<0.05\) were found statistically significant according to agnostic radiomic features. The distribution of 126 features in eight different wavelet filters is 4 low filter band LLH 18 radiomic features, LHL 19 radiomic features, LHH 14 radiomic features, LLL 20 radiomic features and 4 high filter band HLL 11 radiomic features, HLH 19 radiomic features, HHL 13 radiomic features and HHH 12 has radiomic features.