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Lung Cancer Prediction Using Random Forest



In recent years, lung cancer is a common cancer across the globe. For the
early prediction of lung cancer, medical practitioners and researchers require an efficient predictive
model, which will reduce the number of deaths. This paper proposes a lung cancer prediction model
by using the Random Forest Classifier, which aims at analyzing symptoms (gender, age, air pollution,
weight loss, etc.).



This work addresses the problem of classification of lung cancer data using the Random
Forest Algorithm. Random Forest is the most accurate learning algorithm and many researchers in
the healthcare domain use it.



 This paper deals with the prediction of lung cancer by using Random Forest Classifier.



 Proposed method (Random Forest Classifier) applied on two lung cancer datasets, achieved an accuracy of 100% for the lung cancer dataset-1 and 96.31 on dataset-2. In the prediction of lung cancer, the random forest has shown improved accuracy compared with other methods.



This predictive model will help health professionals in predicting lung cancer at an early stage. 

