用机器学习提升口腔癌诊断准确率，神经网络效果最佳。
Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction
- 采用神经网络等算法分析口腔病变数据，自动区分良恶性。
- 神经网络分类准确率达93.6%，优于其他模型。
- 适合医学人工智能研究者和临床辅助诊断开发者。
口腔癌在肿瘤学中构成重大挑战，亟需早期诊断与精准预后以提高患者生存率。近年来，机器学习与数据挖掘技术革新了传统诊断方法，提供了区分良性与恶性口腔病变的智能化、自动化工具。本研究系统综述了神经网络、K-近邻（KNN）、支持向量机（SVM）及集成学习等前沿数据挖掘方法在口腔癌诊断与预后的应用。通过严谨的对比分析，发现神经网络表现最优，预测口腔癌的分类准确率达到93.6%。此外，研究强调融合特征选择与降维技术可进一步提升模型性能。这些成果凸显了先进数据挖掘技术在促进早期检测、优化治疗策略及改善口腔癌患者预后方面的巨大潜力。
原文摘要 · Abstract (English)
Oral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates. Recent advancements in machine learning and data mining have revolutionized traditional diagnostic methodologies, providing sophisticated and automated tools for differentiating between benign and malignant oral lesions. This study presents a comprehensive review of cutting-edge data mining methodologies, including Neural Networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and ensemble learning techniques, specifically applied to the diagnosis and prognosis of oral cancer. Through a rigorous comparative analysis, our findings reveal that Neural Networks surpass other models, achieving an impressive classification accuracy of 93,6 % in predicting oral cancer. Furthermore, we underscore the potential benefits of integrating feature selection and dimensionality reduction techniques to enhance model performance. These insights underscore the significant promise of advanced data mining techniques in bolstering early detection, optimizing treatment strategies, and ultimately improving patient outcomes in the realm of oral oncology.
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