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Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer

机译：微阵列数据的计算智能和乳腺癌的早期诊断的生物医学图像分析

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摘要

The objective of this paper was to perform a comparative analysis of the computational intelligence algorithms to identify breast cancer in its early stages. Two types of data representations were considered: microarray based and medical imaging based. In contrast to previous researches, this research also considered the imbalanced nature of these data. It was observed that the SMO algorithm performed better for the majority of the test data, especially for microarray based data when accuracy was used as performance measure. Considering the imbalanced characteristic of the data, the Naive Bayes algorithm was seen to perform highly in terms of true positive rate {TPR). Regarding the influence of SMOTE, a well-known imbalanced data classification technique, it was observed that there was a notable performance improvement forJ48, while the performance of SMO remained comparable for the majority of the datasets. Overall, the results indicated SMO as the most potential candidate for the microarray and image dataset considered in this research.
机译：本文的目的是对计算智能算法进行比较分析，以识别早期乳腺癌。考虑了两种类型的数据表示形式：基于微阵列和基于医学成像。与以前的研究相比，本研究还考虑了这些数据的不平衡性。可以观察到，SMO算法对于大多数测试数据表现更好，尤其是当使用精度作为性能指标时，对于基于微阵列的数据尤其如此。考虑到数据的不平衡特性，可以认为朴素贝叶斯算法在真实阳性率（TPR）方面表现出色。关于SMOTE（一种众所周知的不平衡数据分类技术）的影响，可以观察到J48的性能有了显着提高，而SMO的性能对于大多数数据集仍然可比。总体而言，结果表明SMO是本研究中考虑的微阵列和图像数据集的最有可能的候选者。

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