Autoregressive parametric modeling combined ANOVA approach for label-free-based cancerous and normal cells discrimination

Abdulgani, Aysha F.; Ahmad, Mahmoud Al

doi:10.1016/j.heliyon.2021.e07027

Cited by 2 publications

(1 citation statement)

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“…Bioelectrical impedance analysis (BIA), bioelectrical impedance spectroscopy (BIS), and bioimpedance tomography (BIT) have firmly established themselves as appropriate modalities for measurement and monitoring to accommodate diagnostics and prognostics in clinical care. With established applications in body composition analysis, where the goal is to aid diagnosis, monitor disease progression, support therapeutic interventions, and better elucidate disease mechanisms, bioimpedance is now being fervently applied at the cellular, − tissue, − and organ levels , with similar goals in mind. While acknowledged within, this review is not concerned with whole body composition analysis to which the reader is directed to excellent recent reviews on this topic. , Of emerging significance is bioimpedance monitoring with simultaneous therapeutic intervention, perioperative and postoperative monitoring, , and surgical guidance, which is the focus of the present review.…”

Section: Introductionmentioning

confidence: 99%

Bioelectrical Impedance Spectroscopy for Monitoring Mammalian Cells and Tissues under Different Frequency Domains: A Review

et al. 2022

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Bioelectrical impedance analysis and bioelectrical impedance spectroscopy (BIA/BIS) of tissues reveal important information on molecular composition and physical structure that is useful in diagnostics and prognostics. The heterogeneity in structural elements of cells, tissues, organs, and the whole human body, the variability in molecular composition arising from the dynamics of biochemical reactions, and the contributions of inherently electroresponsive components, such as ions, proteins, and polarized membranes, have rendered bioimpedance challenging to interpret but also a powerful evaluation and monitoring technique in biomedicine. BIA/BIS has thus become the basis for a wide range of diagnostic and monitoring systems such as plethysmography and tomography. The use of BIA/BIS arises from (i) being a noninvasive and safe measurement modality, (ii) its ease of miniaturization, and (iii) multiple technological formats for its biomedical implementation. Considering the dependency of the absolute and relative values of impedance on frequency, and the uniqueness of the origins of the α-, β-, δ-, and γ-dispersions, this targeted review discusses biological events and underlying principles that are employed to analyze the impedance data based on the frequency range. The emergence of BIA/BIS in wearable devices and its relevance to the Internet of Medical Things (IoMT) are introduced and discussed.

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Section: Introductionmentioning

confidence: 99%

Bioelectrical Impedance Spectroscopy for Monitoring Mammalian Cells and Tissues under Different Frequency Domains: A Review

et al. 2022

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Effects of Stratified Cross-Validation and Hyperparameter Tuning on Sentiment Classification With the Chi2-Rfe Hybrid Feature Selection Technique in the Imdb Dataset

Gautam,

Waoo

2024

ShodhKosh J. Vis. Per. Arts

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Data analysis from social networking sites provides government entities, businesses, and event planners with insights into public sentiments and perceptions. Sentiment analysis (SA) resolves this need by classifying the sentiment of social network users into multiple classes. Despite their usefulness, data from social networking platforms frequently exhibits challenges, including unstructured formats, high volume, and redundant or irrelevant information, which can cause issues like overfitting, underfitting, and the curse of dimensionality. In response to these challenges, this study proposes using the term frequency-inverse document frequency (TF-IDF) for feature extraction along with a hybrid feature selection method that combines Chi2 and recursive feature elimination (RFE), called Chi2-RFE. This approach seeks to identify the optimal feature subset by filtering out irrelevant and redundant features. The proposed method is tested with several classifiers, including KNN, LR, SVC, GNB, DT, and RFC, employing stratified K-fold cross-validation and hyperparameter tuning on an IMDb dataset obtained from Kaggle. By effectively addressing overfitting and underfitting issues, this approach shows that before using StratefiedKfold cross-validation and hyperparameter tuning, LR gives 0.81975 training accuracy and test accuracy 0.815 on training data. After the method mentioned above, overfitting is removed by enhancing accuracy to 0.864833 on test data. KNN also enhanced its test accuracy to 0.891667 from 0.857333. SVC from 0.846666 to 0.883667, and GNB from 0.809666 to 0.829583. Precision is also improved from 0.826 to 0.853 for LR, from 0.848 to 0.897 for KNN, from 0.852 to 0.868 for SVC, and from 0.809666 to 0.799 for GNB. Recall also shows improvement from 0.815 to 0.600 for LR, from 0.857 to 0.894 for KNN, from 0.847 to 0.873 for SVC, and from 0.810 to 0.815 for GNB. F1-score also increased from 0.764 to 0.600 for LR, from 0.843 to 0.883 for KNN, from 0.819 to 0.862 for SVC, and from 0.790 to 0.815 for GNB.

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Autoregressive parametric modeling combined ANOVA approach for label-free-based cancerous and normal cells discrimination

Cited by 2 publications

References 42 publications

Bioelectrical Impedance Spectroscopy for Monitoring Mammalian Cells and Tissues under Different Frequency Domains: A Review

Bioelectrical Impedance Spectroscopy for Monitoring Mammalian Cells and Tissues under Different Frequency Domains: A Review

Effects of Stratified Cross-Validation and Hyperparameter Tuning on Sentiment Classification With the Chi2-Rfe Hybrid Feature Selection Technique in the Imdb Dataset

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