# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 1 Introduction
### Translational Advancements in Machine Learning for Cancer Prediction  
Lung carcinoma, characterized by the malignant growth of lung cells, remains a significant challenge in oncology, emphasizing the need for advancements in diagnostic and predictive technologies. Recent progress in computer vision and data analytics has paved the way for sophisticated diagnostic methods, mainly by analyzing temporal medical images. Beyond imaging, clinical text data, such as patient symptoms and medical histories, have emerged as invaluable resources for lung cancer diagnosis, enabling the integration of diverse data modalities for enhanced prediction. Machine learning (ML) techniques, including neural networks, support vector machines (SVMs), decision trees, convolutional neural networks (CNNs), and nonlinear cellular automata, have shown considerable success in predicting lung cancer recurrence and survivability. Ensemble methods such as Random Forest, XGBoost, Bagging, and Adaboost have demonstrated superior predictive accuracy by combining multiple models to reduce error rates. Public datasets, like the SEER database, have been instrumental in evaluating these methods using metrics such as precision, recall, F1-score, and the Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) curves. Binary classification models have also played a vital role in diagnosis, prognosis, and prediction tasks. Techniques like logistic regression, Gaussian naive Bayes, k-nearest neighbors (KNN), artificial neural networks (ANNs), radial basis function (RBF) networks, gradient-boosted trees, and multilayer perceptrons (MLPs) have been extensively utilized. These models incorporate demographic factors like age and gender and behavioral factors like smoking habits into personalized diagnostic tools. However, significant challenges persist in integrating diverse data types, such as imaging and clinical data, and conducting comprehensive analyses of ML methods. Addressing these challenges requires multidisciplinary approaches to refine diagnostic accuracy and improve treatment planning. The advancements in ML for lung cancer diagnosis also involve integrating traditional and emerging methodologies. Unlike conventional AI, ML evolves by learning from past data, enabling dynamic and complex decision-making. Several studies have highlighted the transformative potential of ML in lung cancer detection and prognosis. [PERSON] et al. [10] identified limitations in existing diagnostic methods, such as the need for advanced feature extraction techniques, robust noise removal, and hybrid classification approaches to improve ML and DL applications in clinical settings. [PERSON] et al. [11] highlighted deep learning techniques, including 2D and 3D CNNs, dual-path networks, and vision transformers (ViTs), which outperform classical ML methods in detecting lung cancer using CT scans. [PERSON] et al. [12] demonstrated that ML models surpass logistic regression in predicting lung cancer survival, offering higher discriminatory accuracy. Similarly, [PERSON] et al. [13] reviewed the strengths and limitations of ML techniques, including deep learning and ensemble methods, for specific imaging types. Other notable contributions include the work of [PERSON] et al. [14], who summarised the effectiveness of CNNs and recurrent neural networks (RNNs) in lung cancer detection while addressing challenges related to higher accuracy and generalizability. [PERSON] et al. [15] showcased the potential of SVMs and Random Forest in predicting lung cancer progression and emphasized their clinical decision-making utility. [PERSON] et al. [16] discussed the challenges of translating ML models for early lung cancer detection into clinical practice, mainly using CT scans. [PERSON] et al. [17] compared deep learning and traditional ML methods, highlighting deep learning models' superior accuracy and sensitivity while stressing the need for better interpretability and generalizability. The literature underscores the transformative potential of ML in lung cancer diagnosis.