Lung cancer is still among the most common and fatal cancers in the world, and thus, there is a need for early and proper detection methods to enhance the survival rate of patients. The recent past has seen machine learning (ML) arise as a promising method for predictive modeling in medical diagnosis with the capability to automate at high levels of accuracy. The current survey gives an extensive review of work done in machine learning approaches of lung cancer diagnosis. It thoroughly goes through different datasets, preprocessing techniques, feature selection techniques, and ML algorithms from supervised and unsupervised to deep learning architectures. Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forests, and ensembles are particularly referred to. Evaluation metrics like precision, recall, accuracy, F1-score, and AUC are presented in bold font to indicate model performance. Concerns like interpretability, data imbalance, and generalization are elaborately mentioned. Hybrid systems and emerging trends like explainable AI (XAI) and transfer learning are also briefly touched upon. Lastly, we highlight the limitations in the literature and offer directions for future research towards building robust, scalable, and clinically relevant ML-based diagnostic systems for lung cancer.
