Advancing lung cancer prediction: A comparative study of ML, DL & hybrid models

One of the deadliest diseases that causes millions of deaths every year is Lung cancer, where early and accurate detection can have a significant part in increasing the survival rate. This research presents among Deep Learning and Machine Learning and a hybrid approach for lung cancer prediction by effectively using the dataset obtained from the trusted source Kaggle, where it consists of several features that are crucial in lung cancer prediction. Three ML models like XGBoost, Random Forest and Support Vector Machine were used in this research and among them XGBoost ranked the highest with an accuracy of 88.50%. Whereas, Deep Learning models such as FullyConnectedNN, CNN1D, and Tab Transformer showed promising results, FullyConnectedNN achieved the highest accuracy of 91.3%. Additionally, hybrid models integrating XGBoost and FCNN achieved the accuracy of 89.20% which is over ML models but it was not able to outperform FCNN, so overall FCNN performed the best among all other approaches.