Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach

TL;DR

This study evaluates various machine learning models, including DNNs and ensemble methods, for lung cancer level classification, emphasizing parameter tuning to enhance diagnostic accuracy and robustness.

Contribution

It provides a comparative analysis of ML algorithms for lung cancer classification, highlighting the effectiveness of DNNs and ensemble methods with optimized parameters.

Findings

DNN models showed robust performance across all phases.

Ensemble methods improved predictive accuracy and robustness.

SVM with Sigmoid kernel faced challenges, indicating room for refinement.

Abstract

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.