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一种预测韩国民众生活满意度的混合自监督模型。

A hybrid self-supervised model predicting life satisfaction in South Korea.

机构信息

Department of Digital Anti-Aging Healthcare (BK21), Inje University, Gimhae, Republic of Korea.

出版信息

Front Public Health. 2024 Oct 17;12:1445864. doi: 10.3389/fpubh.2024.1445864. eCollection 2024.

DOI:10.3389/fpubh.2024.1445864
PMID:39484355
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11524807/
Abstract

OBJECTIVE

Life satisfaction pertains to an individual's subjective evaluation of their life quality, grounded in their personal criteria. It stands as a crucial cognitive aspect of subjective wellbeing, offering a reliable gauge of a person's comprehensive wellbeing status. In this research, our objective is to develop a hybrid self-supervised model tailored for predicting individuals' life satisfaction in South Korea.

METHODS

We employed the Busan Metropolitan City Social Survey Data in 2021, a comprehensive dataset compiled by the Big Data Statistics Division of Busan Metropolitan City. After preprocessing, our analysis focused on a total of 32,390 individuals with 51 variables. We developed the self-supervised pre-training TabNet model as a key component of this study. In addition, we integrated the proposed model with the Local Interpretable Model-agnostic Explanation (LIME) technique to enhance the ease and intuitiveness of interpreting local model behavior.

RESULTS

The performance of our advanced model surpassed conventional tree-based ML models, registering an AUC of 0.7778 for the training set and 0.7757 for the test set. Furthermore, our integrated model simplifies and clarifies the interpretation of local model actions, effectively navigating past the intricate nuances of TabNet's standard explanatory mechanisms.

CONCLUSION

Our proposed model offers a transparent understanding of AI decisions, making it a valuable tool for professionals in the social sciences and psychology, even if they lack expertise in data analytics.

摘要

目的

生活满意度是指个人对自身生活质量的主观评价，基于个人标准。它是主观幸福感的重要认知方面，为衡量一个人的综合幸福感状况提供了可靠的指标。在这项研究中，我们的目标是开发一种针对预测韩国个体生活满意度的混合自监督模型。

方法

我们使用了 2021 年釜山市社会调查数据，这是釜山市大数据统计司编制的一个综合数据集。经过预处理，我们的分析集中在总共 32390 名具有 51 个变量的个体上。我们开发了自监督预训练 TabNet 模型作为本研究的关键组成部分。此外，我们将提出的模型与局部可解释模型不可知解释（LIME）技术相结合，以增强解释局部模型行为的简便性和直观性。

结果

我们的先进模型的性能超过了传统的基于树的 ML 模型，在训练集上的 AUC 为 0.7778，在测试集上的 AUC 为 0.7757。此外，我们的集成模型简化并阐明了局部模型行为的解释，有效地克服了 TabNet 标准解释机制的复杂细微差别。

结论

我们提出的模型提供了对 AI 决策的透明理解，即使社会科学和心理学领域的专业人员缺乏数据分析方面的专业知识，它也是一个有价值的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/2ae1b244cc84/fpubh-12-1445864-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/2fe6b90c4c5a/fpubh-12-1445864-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/9acba7727145/fpubh-12-1445864-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/14e34df89dff/fpubh-12-1445864-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/86ca1b6b10a8/fpubh-12-1445864-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/0e817b7832b4/fpubh-12-1445864-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/7c95fcca9dc1/fpubh-12-1445864-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/a689b71bed54/fpubh-12-1445864-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/4858639b6039/fpubh-12-1445864-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/2ae1b244cc84/fpubh-12-1445864-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/2fe6b90c4c5a/fpubh-12-1445864-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/9acba7727145/fpubh-12-1445864-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/14e34df89dff/fpubh-12-1445864-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/86ca1b6b10a8/fpubh-12-1445864-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/0e817b7832b4/fpubh-12-1445864-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/7c95fcca9dc1/fpubh-12-1445864-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/a689b71bed54/fpubh-12-1445864-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/4858639b6039/fpubh-12-1445864-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7edf/11524807/2ae1b244cc84/fpubh-12-1445864-g009.jpg

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