用特征选择+机器学习提升肺癌早期检测准确率与速度
An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer
- 结合卡方检验筛选关键特征，优化模型输入
- 随机森林与SVM在测试中实现高准确率，缩短运行时间
- 适合医疗AI研究者参考，推动自动化诊断发展
肺癌是致死率最高的癌症之一，早期精准诊断至关重要。本文综述了基于随机森林（RF）和支持向量机（SVM）等先进机器学习技术的肺癌诊断方法。其中，卡方检验作为一种有效的特征选择策略，被用于识别相关特征并提升模型性能。实验结果表明，该组合方法显著提高了检测效率与准确性，同时降低了计算耗时。研究为未来肺癌自动诊断技术的优化提供了可行建议，是推动医疗智能化的重要一步。
原文摘要 · Abstract (English)
One of the deadliest cancers, lung cancer necessitates an early and precise diagnosis. Because patients have a better chance of recovering, early identification of lung cancer is crucial. This review looks at how to diagnose lung cancer using sophisticated machine learning techniques like Random Forest (RF) and Support Vector Machine (SVM). The Chi-squared test is one feature selection strategy that has been successfully applied to find related features and enhance model performance. The findings demonstrate that these techniques can improve detection efficiency and accuracy while also assisting in runtime reduction. This study produces recommendations for further research as well as ideas to enhance diagnostic techniques. In order to improve healthcare and create automated methods for detecting lung cancer, this research is a critical first step.
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