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A Study on Comparison of Lung Cancer Prediction Using Ensemble Machine Learning 원문보기

Korean Journal of Artificial Intelligence = 인공지능연구, v.7 no.2, 2019년, pp.19 - 24  

NAM, Yu-Jin (Dept. of Bio Medical Engineering, Eulji University) ,  SHIN, Won-Ji (Dept. of Radiological Technology, Eulji University)

Abstract ▼ AI-Helper 아이콘AI-Helper

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, t...

주제어

#Lung Cancer   #Machine Learning   #Two-Class Support Vector Machine   #Two-Class Decision Jungle   #Multi-Class Decision Jungle  

AI 본문요약
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* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

제안 방법

  • After that, the name and data type were specified, and the filter was outputted with the optimized properties. In order to split the training data and the test data, the ratio of the split data was set to 7: 3 and based on the classifications, two-class support vector machine, the two-class support vector machine, and the multi-class decision jungle were classified. Training data was used for each of three algorithms, and test data was trained by putting values based on the split data divided above.
  • , Ml algorithms, feature selection and pre-processing) and links them together. It supports about 100 techniques that address ML algorithm, function selection and data preprocessing, navigation, modeling result verification and method selection, regression, classification, text analysis, and recommendations.
  • Step 3: Clean missing data. The goal of such cleaning operation is to prevent problems caused by missing data that can arise during training of a model. In this research, this module is used to replace missing values with a placeholder for mean or other value.
  • Thus, this study reviewed studies regarding artificial intelligence technology that can be used to determine lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparing and analyzing the accuracy of Azure's algorithms provided by Microsoft.
  • 2 (Select columns in Dataset, Edit Metadata, Clean Missing Data, Filter Based Feature Selection) and it is divided into training data and test data by the ratio of 7:3 using Split Data. Training data was used in the purpose of training each algorithm, and test data is used to evaluate the trained algorithms. Overall, Two-Class Support Vector Machine algorithm and the Multi-Class Decision Jungle algorithm are compared.

이론/모형

  • The model in this study was built in Azure ML using three different algorithms: Two-Class Decision Jungle, Multi-Class Decision Jungle and Two-Class Support Vector Machine. Figure 1 shows the final workflow of the algorithm.
본문요약 정보가 도움이 되었나요?

참고문헌 (7)

  1. Aberle, D.R., Adams, A.M., Berg, C.D., Black, W.C., & Clapp, J.D. (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening. The New England Journal of Medicine, 365, 395-409. 

    상세보기 crossref

  2. 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 

  3. Harfoushi, O. (2018). Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison. Modern Applied Science, 12(7), 49-58. 

    상세보기 crossref

  4. Ioannis Kavakiotis, OgaTsave, Athanasios Salifoglou, Nicos, Maglaveras, Ioannis, et al. (2017) Machine Learning and Data Mining Methods in Diabetes Research. Computational and Structural Biotechnology Journal, 15, 104-116. 

    상세보기 crossref

  5. Qasem, M., Thulasiram, R. K., & Thulasiram, P. (2015). Twitter sentiment classification using machine learning techniques for stock markets, IEEE, 10, 834-840. 

  6. Lee, J.G., Jun S., Cho, Y.W., Lee, H., Kim, G.B., Seo, J.B., & Kim, N. (2017). Deep Learning in Medical Imaging: General Overview. Korean Journal of Radiology, 18(4), 570-584. 

    상세보기 crossref

  7. Russell, S., & Norvig, P. (2003). Artificial Intelligence: A Modern Approach (2nd Ed.). New Jersey, USA: Prentice Hall. 

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