PDF(Pubmed)
Abstract：
Respiratory system cancer, encompassing lung, trachea and bronchus cancer, constitute a substantial and evolving public health challenge. Since pollution plays a prominent cause in the development of this disease, identifying which substances are most harmful is fundamental for implementing policies aimed at reducing exposure to these substances. We propose an approach based on explainable artificial intelligence (XAI) based on remote sensing data to identify the factors that most influence the prediction of the standard mortality ratio (SMR) for respiratory system cancer in the Italian provinces using environment and socio-economic data. First of all, we identified 10 clusters of provinces through the study of the SMR variogram. Then, a Random Forest regressor is used for learning a compact representation of data. Finally, we used XAI to identify which features were most important in predicting SMR values. Our machine learning analysis shows that NO, income and O3 are the first three relevant features for the mortality of this type of cancer, and provides a guideline on intervention priorities in reducing risk factors.
摘要：
呼吸系统癌症,包括肺，气管和支气管癌,构成了一个巨大的和不断发展的公共卫生挑战。由于污染在这种疾病的发展中起着重要的作用，确定哪些物质最有害是实施旨在减少接触这些物质的政策的基础。我们提出了一种基于遥感数据的可解释人工智能（XAI）的方法，以使用环境和社会经济数据来识别对意大利各省呼吸系统癌症标准死亡率（SMR）预测影响最大的因素。首先,通过对SMR变异函数的研究，我们确定了10个省份。然后，随机森林回归器用于学习数据的紧凑表示。最后,我们使用XAI来确定预测SMR值最重要的特征.我们的机器学习分析表明，不，收入和O3是这类癌症死亡率的前三个相关特征，并提供了减少风险因素的干预重点指南。
