An Investigational Approach for the Prediction of Gastric Cancer Using Artificial Intelligence Techniques: A Systematic Review

Bhardwaj, Priya; Bhandari, Gaurav; Kumar, Yogesh; Gupta, Surbhi

doi:10.1007/s11831-022-09737-4

Cited by 28 publications

(8 citation statements)

References 95 publications

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“…The lasso technique promotes the use of sparse, basic models (i.e., models with fewer parameters). This form of regression is well suited for models with a high degree of multicollinearity or automating some aspects of model selection, such as variable selection/parameter removal, as necessary [ 37 ]. The mathematical equation of lasso regression is shown in Eq.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

et al. 2022

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In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

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Section: Methodsmentioning

confidence: 99%

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

et al. 2022

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“…WE all know that the healthcare industry yields massive and critical amounts of data in medical records, patient examination results, biomedical research, etc. Handling such data requires an emerging technology like cognitive computing, which allows humans to get interlinked and communicate with the machine to gain insights[ 171 – 173 ]. But to, implementing big cognitive data, especially for healthcare, is a bit complex because it does not show any performance efficiency.…”

Section: Future Directionsmentioning

confidence: 99%

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

Kaur¹,

Kumar

Koul

et al. 2022

Arch Computat Methods Eng

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There is a need for some techniques to solve various problems in today’s computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study’s primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.

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“…Cancer is one of the diseases with the highest mortality rate in the world due to challenges in early detection, rapid metastasis, and severe side effects. 1,2 Nanomedicine has revolutionized cancer treatment by offering novel strategies for drug delivery, diagnosis, and therapy monitoring, 3−5 with advantages of improving curative effect, targeting desirable sites, and reducing side effects. 6−8 With the recent advancements in nanomaterial development, carbon dots (CDs) have emerged as a new class of fluorescent nanomaterials, which possess excellent fluorescence properties and the ability to simultaneously carry multiple drugs through covalent bonds, owing to their diverse surface functional groups such as amino, carboxyl, and hydroxyl groups.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Anticancer Therapy Using pH-Sensitive Carbon Dots-Functionalized Doxorubicin in Cubosomes

Xie,

Wang,

et al. 2024

ACS Appl. Bio Mater.

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Cancer remains a highly lethal disease due to its elusive early detection, rapid spread, and significant side effects. Nanomedicine has emerged as a promising platform for drug delivery, diagnosis, and treatment monitoring. In particular, carbon dots (CDs), a type of fluorescent nanomaterial, offer excellent fluorescence properties and the ability to carry multiple drugs simultaneously through covalent bonding. In this work, CDs with carbonyl groups on the surface were prepared by aldol condensation and reacted with amine groups in the structure of doxorubicin (DOX) through Schiff base reaction to generate pH-responsive CDs-DOX. On the other hand, cubosomes with three-dimensional lattice structures formed by lipid bilayers have advantageous capabilities of encapsulating various hydrophilic, amphiphilic, and hydrophobic substances. The pH-responsive CDs-DOX are subsequently loaded into cubosomes to form an anticancer therapeutic nanosystem,

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An Investigational Approach for the Prediction of Gastric Cancer Using Artificial Intelligence Techniques: A Systematic Review

Cited by 28 publications

References 95 publications

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

Investigation of Anticancer Therapy Using pH-Sensitive Carbon Dots-Functionalized Doxorubicin in Cubosomes

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