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

Gandhi, C. Rajive; Ahmad, Sayed Sayeed; Mehbodniya, Abolfazl; Webber, Julian; Hemalatha, S.; Elwahsh, Haitham; Tiwari, Basant

doi:10.1155/2022/4454226

Cited by 6 publications

(4 citation statements)

References 53 publications

(50 reference statements)

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“…Related scholars have summarized the research on the detection technology of strange users in healthcare social networks. Sun et al [ 8 ] summarized the status quo and associated technologies of abnormal patient profile and abnormal behaviour detection in healthcare social networks. Song et al [ 9 ] focused on analysing malicious patient profile algorithms and their applications based on features, space, and density.…”

Section: Introductionmentioning

confidence: 99%

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Veeramakali

Shobanadevi

Nayak³

et al. 2022

Computational Intelligence and Neuroscience

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A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person’s abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area.

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Section: Introductionmentioning

confidence: 99%

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Veeramakali

Shobanadevi

Nayak³

et al. 2022

Computational Intelligence and Neuroscience

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“…In few cases, the machine learning algorithms, such as neural networks, were also used to process signals ( 11 , 93 , 127 ). Other methods such as Empirical Mode Decomposition, Hilbert-Huang transform, or Independent Component Analysis are used ( 119 , 121 , 128 ).…”

Section: Methods Of Filtration and Analysismentioning

confidence: 99%

Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review

Oczka,

Augustynek,

Penhaker

et al. 2024

Front. Med.

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Electrogastrography (EGG) is a non-invasive method with high diagnostic potential for the prevention of gastroenterological pathologies in clinical practice. In this study, a review of the measurement systems, procedures, and methods of analysis used in electrogastrography is presented. A critical review of historical and current literature is conducted, focusing on electrode placement, measurement apparatus, measurement procedures, and time-frequency domain methods of filtration and analysis of the non-invasively measured electrical activity of the stomach. As a result, 129 relevant articles with primary aim on experimental diet were reviewed in this study. Scopus, PubMed, and Web of Science databases were used to search for articles in English language, according to the specific query and using the PRISMA method. The research topic of electrogastrography has been continuously growing in popularity since the first measurement by professor Alvarez 100 years ago, and there are many researchers and companies interested in EGG nowadays. Measurement apparatus and procedures are still being developed in both commercial and research settings. There are plenty variable electrode layouts, ranging from minimal numbers of electrodes for ambulatory measurements to very high numbers of electrodes for spatial measurements. Most authors used in their research anatomically approximated layout with two++ active electrodes in bipolar connection and commercial electrogastrograph with sampling rate of 2 or 4 Hz. Test subjects were usually healthy adults and diet was controlled. However, evaluation methods are being developed at a slower pace, and usually the signals are classified only based on dominant frequency. The main review contributions include the overview of spectrum of measurement systems and procedures for electrogastrography developed by many authors, but a firm medical standard has not yet been defined. Therefore, it is not possible to use this method in clinical practice for objective diagnosis.Systematic Review Registrationhttps://www.prisma-statement.org/.

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“…SVM also performed well when classifying individuals with functional nausea, with an F1-score of 0.85 [ 5 ]. A neural network approach performed at 95% accuracy to predict un-fed vs. fed states in a population of 1000 human subjects [ 6 ]. Lastly, in our prior ferret study, we achieved > 75% accuracy in a binary or 3-state classification for predicting baseline and early and late periods before emesis; importantly, in our study, electrodes were placed directly on the surface of the stomach [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture

et al. 2023

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Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity have been proposed to play a significant role in these disorders. A significant barrier to usage of these signals in diagnosis and treatment is the lack of consistent relationships between GI myoelectric features and function. A potential cause of this issue is the use of arbitrary classification criteria, such as percentage of power in tachygastric and bradygastric frequency bands. Here we applied automatic feature extraction using a deep neural network architecture on GI myoelectric signals from free-moving ferrets. For each animal, we recorded during baseline control and feeding conditions lasting for 1 h. Data were trained on a 1-dimensional residual convolutional network, followed by a fully connected layer, with a decision based on a sigmoidal output. For this 2-class problem, accuracy was 90%, sensitivity (feeding detection) was 90%, and specificity (baseline detection) was 89%. By comparison, approaches using hand-crafted features (e.g., SVM, random forest, and logistic regression) produced an accuracy from 54% to 82%, sensitivity from 46% to 84% and specificity from 66% to 80%. These results suggest that automatic feature extraction and deep neural networks could be useful to assess GI function for comparing baseline to an active functional GI state, such as feeding. In future testing, the current approach could be applied to determine normal and disease-related GI myoelectric patterns to diagnosis and assess patients with GI disease.

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

Cited by 6 publications

References 53 publications

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review

Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture

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