Big Data Analysis of Lung Cancer Dataset Using Classification
Background of this study was among cancers; lung cancer is a major killer on a global scale. It is essential to accurately classify cancer subtypes in order to determine effective therapy options for lung cancer, a common and fatal disease. The methods used in the study were classification algorithm...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
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| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/19/e3sconf_icsget2025_03010.pdf |
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| author | Tannady Hendy Fernandes Andry Johanes Susanto William Tannady Tan Henny Bin Rakiman Umol Syamsyul |
| author_facet | Tannady Hendy Fernandes Andry Johanes Susanto William Tannady Tan Henny Bin Rakiman Umol Syamsyul |
| author_sort | Tannady Hendy |
| collection | DOAJ |
| description | Background of this study was among cancers; lung cancer is a major killer on a global scale. It is essential to accurately classify cancer subtypes in order to determine effective therapy options for lung cancer, a common and fatal disease. The methods used in the study were classification algorithms for analysing data of lung cancer cases. Lung cancer detection, treatment, and prevention have all come a long way in the last several years, the enhancement of big data method and analysis helps several previous studies that discussed about how big data took important role in medical and health sector. This research was conducted to facilitate the detection of lung cancer based on the symptoms experienced by patients. Result or finding from the study show that RapidMiner’s decision tree algorithm achieved an impressively high level of accuracy, with a Kappa score of 74.32%. This finding proves that the study’s data is reliable enough to identify lung cancer. Result of this study was also stressed the need for habit and symptom-based early detection and diagnosis of lung cancer. |
| format | Article |
| id | doaj-art-45f01e47ded746c2b3ace57a294b4b70 |
| institution | DOAJ |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-45f01e47ded746c2b3ace57a294b4b702025-08-20T03:07:06ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016190301010.1051/e3sconf/202561903010e3sconf_icsget2025_03010Big Data Analysis of Lung Cancer Dataset Using ClassificationTannady Hendy0Fernandes Andry Johanes1Susanto William2Tannady Tan Henny3Bin Rakiman Umol Syamsyul4*Department of Management, Universitas Esa UnggulDepartment of Information System, Universitas Bunda MuliaDepartment of Information System, Universitas Bunda MuliaDepartment of Internal Medicine, Universitas Kristen Krida WacanaDepartment of Business Management, Universiti Teknologi MARABackground of this study was among cancers; lung cancer is a major killer on a global scale. It is essential to accurately classify cancer subtypes in order to determine effective therapy options for lung cancer, a common and fatal disease. The methods used in the study were classification algorithms for analysing data of lung cancer cases. Lung cancer detection, treatment, and prevention have all come a long way in the last several years, the enhancement of big data method and analysis helps several previous studies that discussed about how big data took important role in medical and health sector. This research was conducted to facilitate the detection of lung cancer based on the symptoms experienced by patients. Result or finding from the study show that RapidMiner’s decision tree algorithm achieved an impressively high level of accuracy, with a Kappa score of 74.32%. This finding proves that the study’s data is reliable enough to identify lung cancer. Result of this study was also stressed the need for habit and symptom-based early detection and diagnosis of lung cancer.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/19/e3sconf_icsget2025_03010.pdfbig datalung cancerclassificationdecision tree |
| spellingShingle | Tannady Hendy Fernandes Andry Johanes Susanto William Tannady Tan Henny Bin Rakiman Umol Syamsyul Big Data Analysis of Lung Cancer Dataset Using Classification E3S Web of Conferences big data lung cancer classification decision tree |
| title | Big Data Analysis of Lung Cancer Dataset Using Classification |
| title_full | Big Data Analysis of Lung Cancer Dataset Using Classification |
| title_fullStr | Big Data Analysis of Lung Cancer Dataset Using Classification |
| title_full_unstemmed | Big Data Analysis of Lung Cancer Dataset Using Classification |
| title_short | Big Data Analysis of Lung Cancer Dataset Using Classification |
| title_sort | big data analysis of lung cancer dataset using classification |
| topic | big data lung cancer classification decision tree |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/19/e3sconf_icsget2025_03010.pdf |
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