首页|Lung cancer disease detection using service-oriented architectures and multivariate boosting classifier

Lung cancer disease detection using service-oriented architectures and multivariate boosting classifier

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  • NSTL
  • Elsevier
Big data analytics in healthcare is emerging as a promising field to extract valuable information from large databases and enhance results with fewer costs. Although numerous methods have been proposed for big data analytics in the medical field, an authorized entity is required to access data, inhibiting diagnosis accuracy and efficiency. Particularly, the detection of lung cancer is critical as it is the third most common type of cancer occurring in both males and females in the US and a leading cause of cancer-related deaths worldwide, the detection of lung cancer. Therefore, this study introduces the Multivariate Ruzicka Regressed eXtreme Gradient Boosting Data Classification (MRRXGBDC) technique and service-oriented architecture (SOA) to improve the prediction accuracy and reduce the prediction time of lung cancer in big data analytics. Service-oriented architectures (SOAs) provide a set of healthcare services, where patient data are stored in the database of a physician or other certified entity. After receiving the patient data as input, several multivariate Ruzicka logistic regression trees are constructed by the physician to calculate the relationship between the dependent and independent variables. With this regression analysis, the presence or absence of disease is discovered. The experimental results reveal that the MRRXGBDC technique performs better with 10% improvement in prediction accuracy, 50% reduction of false positives, and 11% faster prediction time for lung cancer detection compared to existing works. (C)& nbsp;2022 Published by Elsevier B.V.

Service-oriented architecturesBig data analyticsDiagnostic problemsMultivariate Ruzicka Logistic Regression treeBase learnerGradient Boost ClassificationGradient descentBIG DATA ANALYTICSSTANDARDIZED SOADECISION-SUPPORTCLASSIFICATIONDIAGNOSISFRAMEWORKSYSTEMCLOUD

Chandrasekar, Thaventhiran、Raju, Sekar Kidambi、Ramachandran, Manikandan、Patan, Rizwan、Gandomi, Amir H.

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SASTRA Deemed Univ

Kennesaw State Univ

Univ Technol Sydney

2022

10.1016/j.asoc.2022.108820

Applied Soft Computing

Applied Soft Computing

EISCI
ISSN：1568-4946
年,卷(期)：2022.122
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