A Comprehensive Analysis on Machine Learning based Methods for Lung Cancer Level Classification

TL;DR

This paper evaluates various machine learning models for accurate lung cancer stage classification, highlighting that traditional models like XGBoost and LGBM outperform deep neural networks in this task.

Contribution

It provides a comprehensive comparison of ML models for lung cancer classification, addressing overfitting and feature correlation to improve diagnostic accuracy.

Findings

Traditional ML models outperform DNN in accuracy and metrics

XGBoost, LGBM, and Logistic Regression achieve superior performance

Models demonstrate potential for reliable lung cancer stage prediction

Abstract

Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigation of the use of machine learning (ML) methods for precise classification of lung cancer stages. A cautious analysis is performed to overcome overfitting issues in model performance, taking into account minimum child weight and learning rate. A set of machine learning (ML) models including XGBoost (XGB), LGBM, Adaboost, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), CatBoost, and k-Nearest Neighbor (k-NN) are run methodically and contrasted. Furthermore, the correlation between features and targets is examined using the deep neural network (DNN) model and thus their capability in detecting complex patternsis established. It is argued that several ML models can be capable of classifying lung cancer stages with great accuracy. In spite of the complexity of DNN architectures, traditional ML models like XGBoost, LGBM, and Logistic Regression excel with superior performance. The models perform better than the others in lung cancer prediction on the complete set of comparative metrics like accuracy, precision, recall, and F-1 score