# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 4 Discussion  
This study highlights the significant role of machine learning and deep learning models in enhancing lung cancer prediction. By combining traditional ML classifiers with deep learning-based feature extraction and optimizing model parameters, the predictive performance of the proposed approach showed notable improvements. The hybrid model, which integrates Gaussian naive Bayes with the AdaBoost algorithm, achieved a high accuracy of 96.42%, surpassing conventional models by addressing key limitations such as feature independence assumptions in naive Bayes classifiers. Compared to existing research, many previous studies have utilized individual ML models like support vector machines (SVMs), decision trees, and naive Bayes for lung cancer prediction. For instance, [PERSON] et al. (2024) reported that the accuracy of Random Forest and Gradient Boosting models was 85.71% and 89.29%, respectively [41]. Our findings indicate substantial advancements, with Random Forest achieving 95.40% and Gradient Boosting reaching 95.61%, confirming the effectiveness of hyperparameter tuning and the application of data  
Figure 5: A comparative analysis between the existing and proposed models.  
augmentation techniques, such as Adaptive Synthetic Sampling (ADASYN), in improving model performance.  
Furthermore, [PERSON] et al. (2023) emphasized the necessity of advanced feature selection and denoising techniques to improve ML-based diagnostic accuracy [10]. Our study aligns with this by employing label encoding and feature selection, resulting in better data representation and minimizing the impact of irrelevant variables. [PERSON] et al. (2022) also demonstrated the advantage of ensemble learning techniques like XGBoost in cancer prediction [15], which is a conclusion that is consistent with our results, where Gradient Boosting emerged as one of the highest-performing classifiers. Deep learning approaches have gained attention in lung cancer diagnostics, particularly medical imaging applications. [PERSON] et al. (2024) demonstrated that CNN-based models outperform conventional ML models for CT scan-based lung cancer detection [11]. While our research focused on structured textual clinical data rather than image-based datasets, incorporating multilayer perceptron (MLP) into our model significantly improved classification accuracy from 89.29% to 94.13%. This finding suggests that DL models can effectively capture complex, nonlinear patterns even in non-imaging datasets, enhancing diagnostic precision.  
Moreover, our study observed an increase in the accuracy of multinomial naive Bayes to 74.84%, indicating that preprocessing improvements addressed some of the model's inherent feature independence constraints. The performance of MLP also improved from 89.29% to 94.13%, likely due to optimized activation functions, deeper network architectures, and regularisation techniques, such as dropout. These results reinforce the notion that deep learning, combined with practical hyperparameter tuning and preprocessing, can yield superior outcomes compared to traditional statistical classifiers.  
The findings of this research further validate the capability of ML models in facilitating early detection of lung cancer and supporting clinical decision-making by identifying high-risk patients based on symptom-based data. The proposed hybrid model, with an accuracy of 96.42%, suggests that ensemble learning techniques can significantly enhance prediction reliability. The ability of ML algorithms to analyse large-scale patient datasets and detect early-stage indicators of lung cancer can transform current diagnostic methods, leading to more personalised and timely treatment interventions.