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      Biosensor-Assisted Method for Abdominal Syndrome Classification Using Machine Learning Algorithm

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          Abstract

          The digestive system is one of the essential systems in human physiology where the stomach has a significant part to play with its accessories like the esophagus, duodenum, small intestines, and large intestinal tract. Many individuals across the globe suffer from gastric dysrhythmia in combination with dyspepsia (improper digestion), unexplained nausea (feeling), vomiting, abdominal discomfort, ulcer of the stomach, and gastroesophageal reflux illnesses. Some of the techniques used to identify anomalies include clinical analysis, endoscopy, electrogastrogram, and imaging. Electrogastrogram is the registration of electrical impulses that pass through the stomach muscles and regulate the contraction of the muscle. The electrode senses the electrical impulses from the stomach muscles, and the electrogastrogram is recorded. A computer analyzes the captured electrogastrogram (EGG) signals. The usual electric rhythm produces an enhanced current in the typical stomach muscle after a meal. Postmeal electrical rhythm is abnormal in those with stomach muscles or nerve anomalies. This study considers EGG of ordinary individuals, bradycardia, dyspepsia, nausea, tachycardia, ulcer, and vomiting for analysis. Data are collected in collaboration with the doctor for preprandial and postprandial conditions for people with diseases and everyday individuals. In CWT with a genetic algorithm, db4 is utilized to obtain an EGG signal wave pattern in a 3D plot using MATLAB. The figure shows that the existence of the peak reflects the EGG signal cycle. The number of present peaks categorizes EGG. Adaptive Resonance Classifier Network (ARCN) is utilized to identify EGG signals as normal or abnormal subjects, depending on the parameter of alertness ( μ). This study may be used as a medical tool to diagnose digestive system problems before proposing invasive treatments. Accuracy of the proposed work comes up with 95.45%, and sensitivity and specificity range is added as 92.45% and 87.12%.

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          Most cited references57

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          Textural Features for Image Classification

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            A GA-based feature selection and parameters optimizationfor support vector machines

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              Texture analysis and classification with tree-structured wavelet transform.

              A multiresolution approach based on a modified wavelet transform called the tree-structured wavelet transform or wavelet packets is proposed. The development of this transform is motivated by the observation that a large class of natural textures can be modeled as quasi-periodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, it is possible to zoom into any desired frequency channels for further decomposition. In contrast, the conventional pyramid-structured wavelet transform performs further decomposition in low-frequency channels. A progressive texture classification algorithm which is not only computationally attractive but also has excellent performance is developed. The performance of the present method is compared with that of several other methods.
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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                28 January 2022
                : 2022
                : 4454226
                Affiliations
                1Department of CSE & IT, Jaypee Institute of Information Technology, Noida, India
                2College of Engineering and Computing, Al Ghurair University, Dubai, UAE
                3Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), Kuwait, Kuwait
                4Graduate School of Engineering Science, Osaka University, Osaka, Japan
                5Department of Computer Science and Engineering, Panimalar Institute of Technology, Chennai, Tamil Nadu, India
                6Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt
                7Department of Computer Science, Hawassa University, Awasa, Ethiopia
                Author notes

                Academic Editor: Mohammed A. A. Al qaness

                Author information
                https://orcid.org/0000-0001-6320-8720
                https://orcid.org/0000-0002-0945-512X
                https://orcid.org/0000-0001-7796-2898
                https://orcid.org/0000-0002-3113-4760
                Article
                10.1155/2022/4454226
                8816582
                35126492
                c1e0be4a-9bb0-43c8-93ab-645b3885e81b
                Copyright © 2022 Charu Gandhi et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 3 November 2021
                : 21 December 2021
                : 28 December 2021
                Categories
                Research Article

                Neurosciences
                Neurosciences

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