对比多种机器学习模型，精准分类肺癌分期。
A Comprehensive Analysis on Machine Learning based Methods for Lung Cancer Level Classification
- 系统测试8种机器学习模型，优化超参数防过拟合。
- XGBoost、LGBM和逻辑回归在准确率等指标上领先。
- 适合医学图像分析与临床辅助诊断研究者参考。
肺癌是全球公共卫生的重大挑战，亟需通过稳定技术实现早期诊断。本文系统研究了多种机器学习（ML）方法在肺癌分期精准分类中的应用。通过谨慎分析，控制最小子节点权重和学习率以缓解模型过拟合问题。系统运行并对比了包括XGBoost（XGB）、LGBM、Adaboost、逻辑回归（LR）、决策树（DT）、随机森林（RF）、CatBoost和k近邻（k-NN）在内的八种机器学习模型。此外，利用深度神经网络（DNN）模型分析特征与目标之间的相关性，验证其捕捉复杂模式的能力。研究指出，多个机器学习模型可实现高精度的肺癌分期分类。尽管深度神经网络结构复杂，但传统机器学习模型如XGBoost、LGBM和逻辑回归表现出更优性能。所有模型在准确率、精确率、召回率和F-1分数等完整评估指标上均表现优异。
原文摘要 · Abstract (English)
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
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