Early Stage Lung Cancer Prediction Using Various Machine Learning Techniques

Lung cancer is one of the most common and serious diseases present around the world, which is observed in people of all age groups ranging from children to old people. Annually it costs a lot of money for the cure and diagnosis of people with lung cancer. The existing clinical techniques such as X-Ray and other imaging procedures require complex hardware and considerable expense. Thus the most important issue is the prediction to be accurate and to use a reliable method for that. This raises the need for (comparatively more effective and cheaper) machine learning models in medical diagnosis using medical data sets. Long-term tobacco smoking results in 85 percent of cases of lung cancer. About 10-15 percent of cases arise in people who never smoked. There are numerous methods and tools that are available now for data analysis and its computation. These technological advancements will be referred and used to develop prediction models in the project to predict the presence of lung cancer at an early stage in a patient.The study involves comparing various classification and ensemble models such as Support Vector Machine(SVM), K- Nearest Neighbor (KNN), Random Forest(RF), Artificial Neural Networks (ANN) and a hybrid model, Voting classifier. The performance of the various models is compared and evaluated in terms of their accuracy. Thus, it is easy to identify a patient with lung cancer at an early stage using various sophisticated technologies of today.