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.
Results
The two best performance results in terms of Area Under the ROC Curve (AUC)
are achieved with a SVM classifier using the top-41 features selected by the
SVM wrapper approach (AUC=0.693) and Logistic Regression with the top-40
features selected by the Pearson (AUC=0.689). Experiments showed that
performing feature selection contributes to classification performance with a
3.9% and 1.9% improvement in AUC for the SVM and Logistic Regression
classifier, respectively, with respect to the results using the full feature
set. The visual approach proposed in this work allows to see that the Neural
Network-based wrapper ranking is the most unstable while the Random Forest is
the most stable.

针对结直肠癌，评估了多种特征排序算法在一组风险预测模型中的性能，通过标量稳定性度量和一种可视化方法评估其稳定性，与专家提供的特征进行了比较分析，并发现了与专家知识相一致的最相关特征。

结直肠癌风险预测模型特征选择的比较研究

A comparative study on feature selection for a risk prediction model for  colorectal cancer

Social determinants of health (SDOH) -- the conditions in which people live,
grow, and age -- play a crucial role in a person's health and well-being. There
is a large, compelling body of evidence in population health studies showing
that a wide range of SDOH is strongly correlated with health outcomes. Yet, a
majority of the risk prediction models based on electronic health records (EHR)
do not incorporate a comprehensive set of SDOH features as they are often noisy
or simply unavailable. Our work links a publicly available EHR database,
MIMIC-IV, to well-documented SDOH features. We investigate the impact of such
features on common EHR prediction tasks across different patient populations.
We find that community-level SDOH features do not improve model performance for
a general patient population, but can improve data-limited model fairness for
specific subpopulations. We also demonstrate that SDOH features are vital for
conducting thorough audits of algorithmic biases beyond protective attributes.
We hope the new integrated EHR-SDOH database will enable studies on the
relationship between community health and individual outcomes and provide new
benchmarks to study algorithmic biases beyond race, gender, and age.

本研究将公共可用的电子病历数据库 MIMIC-IV 与健康社会决定因素（SDOH）特征进行关联，调查这些特征对不同病人群体常见电子病历预测任务的影响，发现社区水平的 SDOH 特征在一般病人群体中无法提高模型性能，但能提高特定亚人群的数据有限模型的公平性；同时，表明 SDOH 特征对于进行超越保护性属性的算法偏见全面审计至关重要。我们希望新的集成型电子病历 - SDOH 数据库能够启动有关社区健康与个人结果之间关系的研究，并提供全新的用于研究超越种族、性别和年龄等算法偏见的基准。