Lung cancer leads to high mortalities in various countries while the reliability of cancer diagnosis has not been paid enough attention. In this work, a novel application of conformal prediction in lung cancer diagnosis with electronic nose is introduced. The nonconformity measurement is based on k-nearest neighbors. In offline prediction, accuracies of 87.5% and 83.33% have been achieved by conformal predictors based on 1NN and 3NN respectively, outperforming those of simple k-nearest neighbor predictors. Additionally, conformal predictors provides confidence and credibility information of each prediction that could inform the patients of diagnostic risks. In online prediction, with increasing number of samples, the frequency of errors given by conformal predictions can gradually be limited by the significance level set by users. This project manifests that electronic nose promises to be an applicable cheaper analytic tool in assisting lung cancer diagnosis and conformal prediction provides a promising method to ensure reliability.

在各个国家，肺癌导致高死亡率，而癌症诊断的可靠性尚未引起足够的重视。在这项工作中，介绍了保形预测在电子鼻肺癌诊断中的新应用。不合格测量基于k最近邻。在离线预测中，基于1NN和3NN的保形预测器分别实现了87.5％和83.33％的准确性，优于简单的k最近邻预测器。另外，保形预测器提供了每个预测的置信度和可信度信息，可以告知患者诊断风险。在在线预测中，随着样本数量的增加，保形预测给出的错误频率可能会逐渐受到用户设置的显着性水平的限制。