Lung Cancer Prediction Using Ensemble Learning
DOI:
https://doi.org//10.32628/CSEIT217357Keywords:
Machine Learning, Lung Cancer, Naive Bayes (NB), Decision Tree, Random Forest, KNN Classifier, Soft Voting, EnsembleAbstract
Lung Cancer is the most commonly occurring type of cancer in the world. Despite all the research in the field of lung cancer is still maintains a extremely high mortality rate and a cure rate of of less than 15%. Majority of lung cancer patients are diagnosed at a very advanced stage which is why randomized clinical trials have come under intense scrutiny from the medical practitioners and have led to a new resurgence of interest in its screening methods and development of newer techniques to improve its efficiency. The existing screening and detection techniques have known to be slow, cost ineffective and have other discrepancies such as false positives. Keeping this in mind we propose to use ensemble learning methods to train our data-set to overcome the drawbacks and improve upon the individual algorithms.
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