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首页> 外文期刊>Radiotherapy and oncology: Journal of the European Society for Therapeutic Radiology and Oncology >Application of the Naive Bayesian Classifier to optimize treatment decisions.
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Application of the Naive Bayesian Classifier to optimize treatment decisions.

机译：朴素贝叶斯分类器在优化治疗决策中的应用。

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摘要

BACKGROUND AND PURPOSE: To study the accuracy, specificity and sensitivity of the Naive Bayesian Classifier (NBC) in the assessment of individual risk of cancer relapse or progression after radiotherapy (RT). MATERIALS AND METHODS: Data of 142 brain tumour patients irradiated from 2000 to 2005 were analyzed. Ninety-six attributes related to disease, patient and treatment were chosen. Attributes in binary form consisted of the training set for NBC learning. NBC calculated an individual conditional probability of being assigned to: relapse or progression (1), or no relapse or progression (0) group. Accuracy, attribute selection and quality of classifier were determined by comparison with actual treatment results, leave-one-out and cross validation methods, respectively. Clinical setting test utilized data of 35 patients. Treatment results at classification were unknown and were compared with classification results after 3 months. RESULTS: High classification accuracy (84%), specificity (0.87) and sensitivity (0.80) were achieved, both for classifier training and in progressive clinical evaluation. CONCLUSIONS: NBC is a useful tool to support the assessment of individual risk of relapse or progression in patients diagnosed with brain tumour undergoing RT postoperatively.
机译：背景与目的：研究朴素贝叶斯分类器（NBC）在评估放疗（RT）后癌症复发或进展的个体风险中的准确性，特异性和敏感性。材料与方法：分析2000年至2005年接受放射治疗的142例脑肿瘤患者的资料。选择了与疾病，患者和治疗有关的96种属性。二进制形式的属性由NBC学习的训练集组成。 NBC计算了被分配给：复发或进展（1）或无复发或进展（0）组的个体条件概率。通过与实际治疗结果，留一法和交叉验证法分别比较，确定准确性，属性选择和分类器质量。临床设置测试利用了35例患者的数据。分类时的治疗结果未知，并与3个月后的分类结果进行比较。结果：分类器训练和进行性临床评估均达到了较高的分类精度（84％），特异性（0.87）和敏感性（0.80）。结论：NBC是一种有用的工具，可支持评估诊断为脑肿瘤术后接受RT的患者的复发或进展风险。

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