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Assessing Feature Selection Techniques for a Colorectal Cancer Prediction Model

机译:评估结直肠癌预测模型的特征选择技术

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Risk prediction models for colorectal cancer play an important role to identify people at higher risk of developing this disease as well as the risk factors associated with it. Feature selection techniques help to improve the prediction model performance and to gain insight in the data itself. The assessment of the stability of feature selection/ranking algorithms becomes an important issue when the aim is to analyze the most relevant features. This work assesses several feature ranking algorithms in terms of performance and robustness for a set of risk prediction models. Experimental results demonstrate that stability and model performance should be studied jointly as RF turned out to be the most stable algorithm but outperformed by others in terms of model performance while SVM-wrapper and the Pearson correlation coefficient are moderately stable while achieving good model performance.
机译:结肠直肠癌风险预测模型发挥着重要作用,以识别患者发展这种疾病的风险较高以及与之相关的风险因素。特征选择技术有助于提高预测模型性能,并在数据本身中获得洞察力。当目标是分析最相关的特征时,特征选择/排名算法稳定性的评估成为一个重要问题。这项工作在一组风险预测模型的性能和鲁棒性方面评估了几种特征排名算法。实验结果表明,应共同研究稳定性和模型性能,因为RF原来是最稳定的算法,但在模型性能方面,在SVM-包装器和Pearson相关系数方面,在实现良好的模型性能的同时,其他人的算法。

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