Discovering the influence of personal features in psychological processes using Artificial Intelligence techniques: the case of COVID19 lockdown in Spain

• URL: http://arxiv.org/abs/2503.05729v1
• Date: Tue, 18 Feb 2025 19:54:26 GMT
• Title: Discovering the influence of personal features in psychological processes using Artificial Intelligence techniques: the case of COVID19 lockdown in Spain
• Authors: Blanca Mellor-Marsa, Alfredo Guitian, Andrew Coney, Berta Padilla, Alberto Nogales,
• Abstract summary: This study analyzes the influence of personal, socioeconomic, general health and living condition factors on psychological states during lockdown using AI techniques.<n>The evaluated models demonstrated strong performance, with accuracy exceeding 80% and often surpassing 90%.
• Score: 1.677718351174347
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: At the end of 2019, an outbreak of a novel coronavirus was reported in China, leading to the COVID-19 pandemic. In Spain, the first cases were detected in late January 2020, and by mid-March, infections had surpassed 5,000. On March the Spanish government started a nationwide lockdown to contain the spread of the virus. While isolation measures were necessary, they posed significant psychological and socioeconomic challenges, particularly for vulnerable populations. Understanding the psychological impact of lockdown and the factors influencing mental health is crucial for informing future public health policies. This study analyzes the influence of personal, socioeconomic, general health and living condition factors on psychological states during lockdown using AI techniques. A dataset collected through an online questionnaire was processed using two workflows, each structured into three stages. First, individuals were categorized based on psychological assessments, either directly or in combination with unsupervised learning techniques. Second, various Machine Learning classifiers were trained to distinguish between the identified groups. Finally, feature importance analysis was conducted to identify the most influential variables related to different psychological conditions. The evaluated models demonstrated strong performance, with accuracy exceeding 80% and often surpassing 90%, particularly for Random Forest, Decision Trees, and Support Vector Machines. Sensitivity and specificity analyses revealed that models performed well across different psychological conditions, with the health impacts subset showing the highest reliability. For diagnosing vulnerability, models achieved over 90% accuracy, except for less vulnerable individuals using living environment and economic status features, where performance was slightly lower.

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