Predictive Insights: Empowering Early Detection of Lung Cancer Using Machine Learning Excellence

Recognition and prediction of lung cancer at the earliest possible stage can significantly increase patient survival. But it is challenging to identify lung cancer's early symptoms and predict cancer-based on them. This paper implements a machine learning algorithm to predict lung cancer by using the most correlated symptoms of lung cancer. By applying data visualization and feature extraction techniques, we found the most correlated symptoms of lung cancer and predicted them using logistic regression, support vector machine, and k nearest neighbor classifiers; among them, support vector machine and logistic regression both showed higher accuracy in lung cancer detection, with 98.52 % accuracy in the training set and 98.39 % accuracy in the testing set.