## 3 Results and Discussion  
In this study, we first computed the GLCM features from heart failure signals. We determined these features based on ROC values. The highly important dissimilarity value was selected as our target node, and its association with other extracted target variables was computed. The detailed analysis will help to elucidate further dynamics of our complex analysis for a deeper understanding of the extracted features. We first extracted the GLCM features from pituitary and meningioma brain MRI images. We then ranked the features based on entropy values. The ranked values, based on entropy, are shown in Figure 2. The importance of the features based on entropy value are energy (3.069), hoomogenity1 (2.7317), homogenity2 (2.6927), maximum probability (2.6818), and so on. We then chose energy as the target variable to compute its association with other computed GLCM features using a Bayesian inference approach. Figure 3 shows the arc analysis using the _M_I method. The arcs between the nodes show the strength of the relationship between the nodes. The bolder arc with greater values shows the highest strength between the nodes; the arc line decreases accordingly as the value and strength of the relationship decreases. There are also other methods for computing arc analysis, including Pearson's correlation (PC), etc. However, in the present study, we only utilized the _M_I for our arc analysis. Moreover, node size can be computed using different methods, including mean, normalized mean value, node force, etc., to reflect the node size. However, in this study, the node size was computed using the normalized mean value. Figure 2: Feature ranking based on entropy values. In this study, we computed the GLCM features and computed the associations among them using mutual information, as reflected in Figure 3. The arcs show the strengths of relation, and the nodes show the normalized mean values. The relationship of the highest strength was yielded by correlation \(\rightarrow\) correlation\(1,1.4640\), followed by dissimilarity \(\rightarrow\) homogenity\(1,1.2164\), energy \(\rightarrow\) entropy, \(1.0845\), and so on. Table 1 shows the outgoing, incoming, and total force of extracted GLCM features from congestive heart failure signals. The highest incoming force was yielded at node dissimilarity (1.0146), outgoing force (0.9330), and total force (1.9477), followed by cluster shade with incoming force (0.9330), outgoing force (0.000), and total force (0.9330); this contrasts with incoming force (0.6281), outgoing force (0.5446), and total force (1.1727), and so on. The target node of dissimilarity had a probability of 58.62% and a joint probability of 100%. The first level of the tree contains child nodes with probabilities such as cluster prominence (100%), contrast (100%), cluster shade (91.78%), energy (73.53%), entropy (68.23%), homogenity1 (68.23%), autocorrelation (67.79%), correlation (63.95%), and correlation1 (62.51%). The red colors show the 1/3 rd cluster, green shows the 3/3 rd cluster ranges. Navy blue color denote the probabilities at each node. Light purple denotes the joint probability and white denote the scores as reflected in Figure 4.  
Figure 3: Arc analysis mutual information (_MI_) with node size of normalized mean. At the second level, cluster shades produce child nodes with probabilities such as energy (95.38%), entropy (94.21%), homogenity1 (94.21%), autocorrelation (93.56%), correlation (92.91%), and correlation1 (92.64%). Likewise, energy has child nodes of autocorrelation, correlation, and correlation2. Moreover, homogeneity1 has child nodes of autocorrelation, correlation, and correlation2. At the third level, energy, entropy, and homogenity1 have the child nodes autocorrelation, correlation, and correlation2. Figure 5 shows the unsupervised clustering when dissimilarity was the selected target node. The arrows indicate the direction of relationship. Further dynamics are computed based on the target node's associations with other nodes.