Using the Random Forest Model to Improve Lung Cancer Diagnosis and Prognosis: A Pilot

There have been increasing research interests and efforts on the application of IoT network to healthcare systems. Our study primarily highlights the effectiveness of the Random Forest (RF) model in analyzing Lung cancer datasets and recognizing trends and patterns. Our study can detect different stages of lung cancer and predict the disease progression. Our work can offer automated and personalized diagnosis using a machine learning approach. The effectiveness of the random forest algorithm was used in the detection of lung cancer, which is an important problem area in the healthcare system, and successful results were obtained. Our approach can provide useful advice to healthcare professionals as a decision support method. Performance metrics such as accuracy, precision, recall and F1 scores were collected to measure the effectiveness of the model. This research has great potential in the diagnosis and treatment of lung cancer patients with low errors and fast speed.Keywords: Lung cancer prediction, lung cancer classification, machine learning, random forest.