## 2 Literature Review
### Research Deficiencies and Prospective Avenues  
The efficacy of lung cancer prediction extends beyond ML model selection and necessitates integrating patient-specific information, including symptoms, lifestyle habits, and genetic predispositions. Smoking is the primary cause of lung cancer, responsible for 80-90% of the occurrences in the United States [1,2]. Therefore, ML models must include these characteristics to improve their predictive accuracy.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Source** & **ML/DL Methods** & **Data source** & **Focus** & **Key Findings** \\ \hline [PERSON] et al. (2024) [1] & Various ML algorithms & Kaggle & Comparative analysis of ML algorithms for lung cancer prediction & Based on classification and heat map correlation analysis, the KNN and NB models were identified as the most significant methods for early lung cancer prediction. \\ \hline [PERSON] and [PERSON] (2021) [11] & Various ML algorithms & UCI Machine learning repository & Comparison of ML algorithms for diabetes prediction & Feature selection influences the ML model disease prediction. \\ \hline [PERSON] et al. (2023) [12] & ML & Computer network behavior data & Numerical feature selection in anomaly detection & Feature selection is important in developing correct and effective diagnostic models. \\ \hline [PERSON] (2024) [13] & ML & Unspecified & Graphical representation of data prediction. & Data visualization is important in supporting the understanding of the underlying factors that contribute to lung diseases. \\ \hline [PERSON] (2020) [14] & SVM, LR, RF, DT, NB, KNN & UCI machine learning repository & Lung cancer prediction & The proposed RBF classifier achieved an accuracy of 81.25\%. \\ \hline [PERSON] et al. (2019) [15] & LR, SVM, NB, DT & Unspecified & Lung cancer detection & Ensemble classifier performed the best, followed by SVM. NB performed the worst. \\ \hline [PERSON] et al. (2021) [17] & CNNs & Medical images & Deep learning-enabled medical computer vision & Healthcare ML success depends on data assessment, limitation planning, community participation, and trust. \\ \hline [PERSON] et al. (2021) [8] & DL & Reviewing published materials & A holistic approach is necessary for a comprehensive understanding of DL. \\ \hline [PERSON] et al. (2024) [6] & ML, DL & Reviewing the papers & Advances in a data-driven paradigm shift & The challenges associated with implementing ML and DL in healthcare. \\ \hline [PERSON] et al. (2023) [9] & Mathematical Models & Unspecified & Research on the usage of NN, ML, and statistical models for medical problems & Role of mathematical models in medicine. \\ \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of ML and DL approaches in lung cancer prediction research.  
Despite the effectiveness of ML and DL models in lung cancer prediction, challenges remain. Many studies rely on small or imbalanced datasets, hindering the result generalizability. Additionally, the computational complexity and large data requirements of deep learning models pose obstacles to clinical application.  
Future investigations need to include feature selection to pinpoint the most relevant features and explainable ML and DL models to enhance the transparency in decision making. Moreover, using extensive datasets helps improve the model resilience and relevance across distinct patient demographics.