## 4 Discussion  
The powerful and robust ensemble machine learning methods, such as support vector regression (SVR), SVR with linear kernel, SVR with RBF kernel, AdaBoost adaptive algo \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{3}{c}{**Configuration-1 (Leave-One-Out)**} & **Configuration-II (5-fold)** & **Configuration-III (Split-Train-Test0)** \\ \hline High-grade gliomas & & & \\ \hline Rad & 321.33 & 269.47 & 254.08 \\ Path & 225.62 & 226.99 & 237.50 \\ Rad-path & 212.72 & 190.13 & 193.65 \\ \hline Low-grade gliomas & & & \\ \hline Rad & 757.97 & 741.48 & 590.54 \\ Path & 621.49 & 670.48 & 543.00 \\ Rad-path & 502.18 & 609.69 & 498.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 3: Kaplan–Meier curves for survival experiment. The x-axis shows the actual in days and the y-axis shows the cumulative survival probability. Left to right, respectively, show classification results of LOO, 5-fold and split-train-test configurations. Top to bottom show HGGs + LGGs, HGGs, and LGGs datasets, respectively. rithm, gradient boost boosting method, and random forest approaches, are employed in this research study. These are used to obtain maximum accuracy and significance for finding the better prediction of survival outcomes of glioma patients. Different high-resolution images show higher complexity and nonlinear dynamics. These require multi-dimensional feature extraction techniques for prediction from an image due to large variations in shape and size required in multi-dimension feature extraction techniques to effectively and significantly performance. Therefore, the feature extracting strategies, such as LBP, GLCM, GLSZM, GLRLM, and NGTDM, are employed to overcome the hurdles faced by the prediction problems. To achieve robustness in survival prediction, the novel ensemble regression machine learning methodologies, such as SVR, SVR (with various kernels), AdaBoost, gradient boost, and random forest approaches are developed in Python version 3.9.5. The ensemble regression methods were applied in different configurations, including split-train-test validation, 5-fold cross-validation, and leave-one-out cross-validation to evaluate the generalizability of the model. Pearson's correlation coefficient was used as a performance measure to evaluate the performance of the model. The datasets involved in this work are a low-grade glioma LGGS dataset, high-grade glioma dataset, and are grouped by combining both these LGGs and HGGs datasets. The radiology (rad) images, digital pathology (path) images, and the combination of image sets, namely rad-path images, are utilized for better performance and to obtain salient features for better prognoses of glioma patients. In past research works, the working is upon either the radiographic images or upon digital pathology images for the survival prediction. However, in this study, the obtained results reportedly show that the current feature extraction strategies (LBP, GLCM, GLSZM, GLRLM, and NGTDM) deliver the most promising results. The novelty in this study is that the combining of both types of images of radiology and pathology domains outbursts the results by employing ensemble machine learning regression approaches. The results are more effective and can play a very important and more significant role by predicting more accurately and can be helpful in clinical diagnosis and treatments. In previous work, authors suggested that T1-Gd intensity is a measure of breakdown of blood-brain barrier and serves as an important predictor of overall survival in gliomas. In our regression survival model, patients predicted with higher score, and lower volumes of enhancing tumor, higher intensities in FLAIR and T2 images, reflecting reduced cell density and increased concentration of fluid. The entropy and homogeneity are other vital features, which quantify randomness in an image. The invasion and diffused nature of the tumor were measured in terms of volume and shape features.