Zoish: A Novel Feature Selection Approach Leveraging Shapley Additive Values for Machine Learning Applications in Healthcare
Zoish: A Novel Feature Selection Approach Leveraging Shapley Additive Values for Machine Learning Applications in Healthcare
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DOI：
10.1142/9789811286421_0007
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发表时间：
2023-12
影响因子：
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通讯作者：
H. Sadaei;Salvatore Loguercio;Mahdi Shafiei Neyestanak;Ali Torkamani;Daria Prilutsky
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文献类型：
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作者：
H. Sadaei;Salvatore Loguercio;Mahdi Shafiei Neyestanak;Ali Torkamani;Daria Prilutsky
在医疗保健分析的复杂环境中，有效的特征选择是生成强大预测模型的先决条件，特别是考虑到样本大小和潜在偏差的共同挑战。Zoish独特地解决了这些问题，采用Shapley加性值-一个想法植根于合作博弈论-使透明和自动化的功能选择。与现有工具不同，Zoish是通用的，旨在与一系列机器学习库无缝集成，包括scikit-learn，XGBoost，CatBoost和imbalanced-learn。Zoish的独特优势在于其计算Shapley值的双重算法方法，使其能够有效地管理大型和小型数据集。这种适应性使其非常适合广泛的医疗保健相关任务。该工具还非常强调可解释性，为分析的功能提供全面的可视化。其可定制的设置为用户提供了对功能选择的细粒度控制，从而优化了特定的预测目标。这份手稿阐明了支持Zoish的数学框架，以及它如何将局部和全局特征选择独特地结合到一个单一的流线型过程中。为了验证Zoish的效率和适应性，我们提出了乳腺癌预测和蒙特利尔认知评估（莫卡）预测帕金森病的案例研究，沿着300个合成数据集的评估。这些应用强调了Zoish在不同医疗保健环境中及其同行中无与伦比的性能。
In the intricate landscape of healthcare analytics, effective feature selection is a prerequisite for generating robust predictive models, especially given the common challenges of sample sizes and potential biases. Zoish uniquely addresses these issues by employing Shapley additive values—an idea rooted in cooperative game theory—to enable both transparent and automated feature selection. Unlike existing tools, Zoish is versatile, designed to seamlessly integrate with an array of machine learning libraries including scikit-learn, XGBoost, CatBoost, and imbalanced-learn. The distinct advantage of Zoish lies in its dual algorithmic approach for calculating Shapley values, allowing it to efficiently manage both large and small datasets. This adaptability renders it exceptionally suitable for a wide spectrum of healthcare-related tasks. The tool also places a strong emphasis on interpretability, providing comprehensive visualizations for analyzed features. Its customizable settings offer users fine-grained control over feature selection, thus optimizing for specific predictive objectives. This manuscript elucidates the mathematical framework underpinning Zoish and how it uniquely combines local and global feature selection into a single, streamlined process. To validate Zoish’s efficiency and adaptability, we present case studies in breast cancer prediction and Montreal Cognitive Assessment (MoCA) prediction in Parkinson’s disease, along with evaluations on 300 synthetic datasets. These applications underscore Zoish’s unparalleled performance in diverse healthcare contexts and against its counterparts.