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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >A comparative study on feature selection for a risk prediction model for colorectal cancer
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A comparative study on feature selection for a risk prediction model for colorectal cancer

机译：结直肠癌风险预测模型特征选择的比较研究

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摘要

Background and objective: Risk prediction models aim at identifying people at higher risk of developing a target disease. Feature selection is particularly important to improve the prediction model performance avoiding overfitting and to identify the leading cancer risk (and protective) factors. Assessing the stability of feature selection/ranking algorithms becomes an important issue when the aim is to analyze the features with more prediction power.Methods: This work is focused on colorectal cancer, assessing several feature ranking algorithms in terms of performance for a set of risk prediction models (Neural Networks, Support Vector Machines (SVM), Logistic Regression, k-Nearest Neighbors and Boosted Trees). Additionally, their robustness is evaluated following a conventional approach with scalar stability metrics and a visual approach proposed in this work to study both similarity among feature ranking techniques as well as their individual stability. A comparative analysis is carried out between the most relevant features found out in this study and features provided by the experts according to the state-of-the-art knowledge.
机译：背景和目的：风险预测模型旨在识别人们越来越高的发展目标疾病的风险。特征选择尤为重要，可以提高预测模型性能避免过度装备，并识别主要的癌症风险（和保护性）因素。评估特征选择/排名算法的稳定性成为一个重要问题，当目的是分析具有更多预测电力的特征。预测模型（神经网络，支持向量机（SVM），逻辑回归，k最近邻居和升级树）。另外，在具有标量稳定度量的传统方法之后评估它们的鲁棒性，并且在这项工作中提出的视觉方法来研究特征排名技术之间的相似性以及它们的各个稳定性。在本研究中发现的最相关的特征和专家提供的特征之间进行了比较分析，根据最先进的知识。

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