Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach
This work addresses improving diagnostic accuracy and prognosis in lung cancer for oncological care, but it is incremental as it compares existing methods with parameter tuning.
The paper tackled lung cancer level classification using machine learning models, finding that Deep Neural Networks performed robustly across all phases, while ensemble methods like voting and bagging enhanced predictive accuracy, though Support Vector Machines with Sigmoid kernel faced challenges.
This paper explores machine learning (ML) models for classifying lung cancer levels to improve diagnostic accuracy and prognosis. Through parameter tuning and rigorous evaluation, we assess various ML algorithms. Techniques like minimum child weight and learning rate monitoring were used to reduce overfitting and optimize performance. Our findings highlight the robust performance of Deep Neural Network (DNN) models across all phases. Ensemble methods, including voting and bagging, also showed promise in enhancing predictive accuracy and robustness. However, Support Vector Machine (SVM) models with the Sigmoid kernel faced challenges, indicating a need for further refinement. Overall, our study provides insights into ML-based lung cancer classification, emphasizing the importance of parameter tuning to optimize model performance and improve diagnostic accuracy in oncological care.