Lung Cancer Predictive Analysis Using Optimized Ensemble and Hybrid Machine Learning Techniques (original) (raw)

Lung cancer is one of the most frequent causes of death globally, mainly attributed to inadequate diagnostic methods and treatment opportunities. Detecting diseases at an early stage greatly improves the chances of survival, and even higher accuracy can be achieved with the help of machine learning tools. The work described in this study proposes the development of an optimized model for predicting lung cancer using deep learning and assembly on the public dataset. The models used include ResNet101 and VER-Net, as well as an ensemble model, CatBoost, and Random Forest with data oversampling done through ADASYN. To compare the effectiveness of the models, evaluation measures included Accuracy, Precision, Recall, F1-Score, and AUC. Among all the models, VER-Net reveals the highest results with an Accuracy of 97%, AUC of 98%, and Precision of 100%. These results suggest how our models could aid in early lung cancer detection. This approach will be extended to other health conditions in future work, and it will attempt to augment the models' performance by increasing the size of the data set and exploring federated learning for data privacy purposes.