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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.
목차
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참고문헌 (7)
참고문헌 신청
Aberle, D. R. / 2011 / Reduced lung-cancer mortality with low-dose computed tomographic screening / The New England Journal of Medicine 365:395~409
Alam, T. M. / 2019 / Cervical Cancer Prediction through Different Screening Methods using Data Mining(IJACSA) / International Journal of Advanced Computer Science and Applications 10(2):388~396
Harfoushi, O. / 2018 / Sentiment Analysis Algorithms through Azure Machine Learning : Analysis and Comparison / Modern Applied Science 12(7):49~58
Ioannis Kavakiotis / 2017 / Machine Learning and Data Mining Methods in Diabetes Research / Computational and Structural Biotechnology Journal 15:104~116
Qasem, M. / 2015 / Twitter sentiment classification using machine learning techniques for stock markets / IEEE 10:834~840
Alam, T. M. / 2019 / Cervical Cancer Prediction through Different Screening Methods using Data Mining(IJACSA) / International Journal of Advanced Computer Science and Applications 10(2):388~396
Harfoushi, O. / 2018 / Sentiment Analysis Algorithms through Azure Machine Learning : Analysis and Comparison / Modern Applied Science 12(7):49~58
Ioannis Kavakiotis / 2017 / Machine Learning and Data Mining Methods in Diabetes Research / Computational and Structural Biotechnology Journal 15:104~116
Qasem, M. / 2015 / Twitter sentiment classification using machine learning techniques for stock markets / IEEE 10:834~840
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