Facing multidimensional poverty in older adults: An artificial intelligence approach that reveals the variable relevance

Lorenzo Olearo, Fabio D'Adda, Enza Messina, Marco Cremaschi, Stefania Bandini, Francesca Gasparini
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Abstract

Despite the rapid development in very recent years of Artificial Intelligence models to predict poverty risk, this problem still remains an unsolved open challenge, especially from a multidimensional perspective. One of the main challenges is related to the scarcity of labelled and high-quality data for training models coupled with the lack of a general reference model to build good predictors. This results in the proposal of a variety of approaches tailored to specific contexts. This paper presents our proposal to address multidimensional poverty prediction, starting from an unlabelled dataset. We focus on the case of a fragile population, the older adults; our approach is highly flexible and can be easily adapted to various scenarios. Firstly, starting from expert knowledge, we apply a stochastic method for estimating the probability of an individual being poor, and we use this probability to identify three levels of risk. Then, we train an XGBoost classification model and exploit its tree structure to define a ranking of feature relevance. This information is used to create a new set of aggregated features representative of different poverty dimensions. An explainable novel Naive Bayes model is then trained for predicting individuals’ deprivation level in our particular domain. The capacity to identify which variables are predominantly associated with poverty among older adults offers valuable insights for policymakers and decision-makers to address poverty effectively.
面对老年人的多维贫困:揭示变量相关性的人工智能方法
尽管近年来预测贫困风险的人工智能模型发展迅速,但这一问题仍然是一个尚未解决的公开挑战,尤其是从多维角度来看。其中一个主要挑战是,用于训练模型的高质量标签数据匮乏,同时缺乏建立良好预测模型的通用参考模型。因此,我们提出了各种针对具体情况的方法。本文介绍了我们从无标签数据集出发,解决多维贫困预测问题的建议。我们将重点放在老年人这一脆弱人群的案例上;我们的方法非常灵活,可以很容易地适应各种情况。首先,我们从专家知识出发,采用随机方法估算个人贫困的概率,并利用该概率确定三个风险等级。然后,我们训练一个 XGBoost 分类模型,并利用其树状结构来确定特征相关性的等级。这些信息被用来创建一组新的综合特征,代表不同的贫困维度。然后训练出一个可解释的新型 Naive Bayes 模型,用于预测个人在我们特定领域的贫困程度。确定哪些变量主要与老年人的贫困相关的能力为政策制定者和决策者有效解决贫困问题提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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