Big Data Analysis of Lung Cancer Dataset Using Classification - Tannady Hendy; Fernandes Andry Johanes; Susanto William; Tannady Tan Henny; Bin Rakiman Umol Syamsyul (E3S Web of Conferences)
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.