A Bayesian Dynamic Inference Approach Based on Extracted Gray Level Co-Occurrence (GLCM) Features for the Dynamical Analysis of Congestive Heart Failure

Abstract

The adoptability of the heart to external and internal stimuli is reflected by heart rate variability (HRV). Reduced HRV can be a predictor of post-infarction mortality. In this study, we propose an automated system to predict and diagnose congestive heart failure using short-term heart rate variability analysis. Based on the nonlinear, nonstationary, and highly complex dynamics of congestive heart failure, we extracted multimodal features to capture the temporal, spectral, and complex dynamics. Recently, the Bayesian inference approach has been recognized as an attractive option for the deeper analysis of static features, in order to perform a comprehensive analysis of extracted nodes (features). We computed the gray level co-occurrence (GLCM) features from congestive heart failure signals and then ranked them based on ROC methods. This study focused on utilizing the dissimilarity feature, which is ranked as highly important, as a target node for the empirical analysis of dynamic profiling and optimization, in order to explain the nonlinear dynamics of GLCM features extracted from heart failure signals, and distinguishing CHF from NSR. We applied Bayesian inference and Pearson’s correlation (PC). The association, in terms of node force and mapping, was computed. The higher-ranking target node was used to compute the posterior probability, total effect, arc contribution, network profile, and compression. The highest value of ROC was obtained for dissimilarity, at 0.3589. Based on the information-gain algorithm, the highest strength of the relationship was obtained between nodes “dissimilarity” and “cluster performance” (1.0146), relative to mutual information (81.33%). Moreover, the highest relative binary significance was yielded for dissimilarity for 1/3rd (80.19%), 2/3rd (74.95%) and 3/3rd (100%). The results revealed that the proposed methodology can provide further in-depth insights for the early diagnosis and prognosis of congestive heart failure.

Intelligent lung cancer MRI prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent Bayesian methods on extracted gray-level co-occurrence (GLCM) features

“…In short, computer-aided diagnosis of lung nodules is likely to have an important role in CT-based screening tests in the near future. 58 The present research is consistent with similar studies investigated by utilizing BayesiaLab to determine the strength of relationship, causality among the features, node force, dynamic profile analysis, sensitivity analysis to further unfold the nonlinear hidden dynamics to further improve the diagnostic and prognostic such as brain tumor MRIs, 12 congestive heart failure, 13 prostate cancer, 59 and lungs infection due to particulate matters. 15 The results reveal that the proposed approach can be better utilized for early decision-making and improving the better diagnostic and prognostic system for patients suffering from lung pathologies.…”

“… 7 BNs have successfully been utilized in many studies by different researchers such as Kocian et al, 8 Amaral et al, 9 Laurila-Pant et al, 10 and Zhang et al 11 The Bayesian inference (BI) approach utilizing the BayesiaLab has gained its popularity to investigate the causal relationships, dynamic profile analysis, target analysis, posterior probability analysis, etc. in variety of different applications including empirical analysis of brain tumor MRIs, 12 dynamic profile analysis of congestive heart failure, 13 environmental sustainability indicators to manufacture, 14 dynamical analysis of particulate matters future challenges monitoring and evaluations, 15 predicting childhood lead exposure risk from community water system using Bayesian approach, 16 etc. There are many applications of BI approach such as intrahepatic cholangiocarcinoma patients’ microvascular invasion, 17 human factor analysis for sustainable hazardous cargo port operations, 18 gallbladder polyps with malignant potential based on preoperative ultrasound Bayesian prediction analysis, 19 decision-making, 20 industries 4.0 technologies selection, 21 transport vulnerability, 22 etc.…”

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

“…The Bayesian theorem forms the basis of BO [ 41 , 42 ]. In order to update the optimization function posterior, it establishes a prior over the optimization function and collects information from the previous sample set [ 43 ].…”

A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease

“…The initial changes are carried out after the birth of a child, causing a disorder in the functioning of the heart Eltahir, M.M. et al [22] In clinical practice, acquired heart defects are classified in different ways. The white type of CHD is triggered by stimulation of the skin.…”

“…In some cases, repairing a simple heart defect may be possible through cardiac catheterization in a special lab, but more complex defects may require open-heart surgery [ 21 ]. Atrial septal defect, ventricular septal defect, and patent ductus arteriosus are some of the most effective forms of CHD that require surgery [ 22 ]. Different techniques are used to repair these defects, such as using different devices to reduce the effects of air or to close up the patent ductus arteriosus [ 23 ].…”