The paper aims to assess breast cancer prognostic markers and to determine an optimum subset that can yield high prediction accuracy for an individual breast cancer patient's prognosis by means of a fuzzy measurement derived from the fuzzy k-nearest neighbour algorithm (FK-NN). The analyses are carried out for both nodal involvement and five-year survival. The data set used for the analysis of breast cancer prognosis consists of seven input markers (histology type, grade, DNA ploidy, S-Phase Fraction (SPF), G/sub 0/G/sub 1//G/sub 2/M ratio, minimum and maximum nuclear pleomorphism indices (NPI)) and two corresponding outputs to be predicted (negative or positive nodal status in the case of nodal involvement assessment, and whether the patient is alive or dead within 5 years of diagnosis for survival analysis). The highest predictive accuracy is 78% with the fuzzy measurement of 0.7254 for nodal involvement assessment, and 88% with the fuzzy measurement of 0.8183 for survival analysis. The best results are obtained from the subset (Histology type, Grade, DNA. Ploidy, SPF (%), G/sub 0/G/sub 1//G/sub 2/M Ratio) for survival prediction and the subset (Grade, SPF, minimum NPI) for nodal involvement analysis.
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机译：本文旨在评估乳腺癌的预后指标，并确定最佳子集，该子集可以通过从模糊k最近邻居算法（FK-NN）得出的模糊测量来为单个乳腺癌患者的预后提供较高的预测准确性。进行了淋巴结转移和五年生存期的分析。用于分析乳腺癌预后的数据集包括七个输入标记（组织学类型，等级，DNA倍性，S相分数（SPF），G / sub 0 / G / sub 1 // G / sub 2 / M）比率，最小和最大核多态性指数（NPI））以及可预测的两个相应输出（在淋巴结受累评估的情况下淋巴结呈阴性或阳性状态，以及在进行生存分析的诊断后5年内患者是否生还或死亡） 。淋巴结浸润评估的最高预测准确度为78％（模糊度为0.7254），生存分析的最高预测准确度为88％（模糊度为0.8183）。从用于生存预测的子集（组织学类型，等级，DNA。倍性，SPF（％），G / sub 0 / G / sub 1 // G / sub 2 / M比）获得最佳结果，并获得子集（等级，SPF，最低NPI）用于节点参与度分析。
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