International journal of medical informatics · 2025
Exploring the importance of clinical and sociodemographic factors on self-rated health in midlife: A cross-sectional study using machine learning.
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Passarelli-Araujo Hisrael
Аннотация
BACKGROUND: Self-rated health (SRH) is influenced by various factors, including clinical and sociodemographic characteristics. However, in the context of Brazil, we still lack a clear understanding of the relative importance of these factors and how they differ between men and women in midlife. Given the significance of gender equity in health, it is crucial to explore these differences to meet the specific needs of each group.
OBJECTIVE: This study examines the importance of clinical and sociodemographic factors of SRH among middle-aged Brazilian adults and analyzes how they vary between men and women.
METHODS: A cross-sectional analysis was conducted using data from the 2019 National Health Survey (PNS) with a representative sample of 31,926 middle-aged adults (40-59 years) living in private households on Brazilian territory. Five machine learning models-Naive Bayes, SVM, Logistic Regression, Random Forests, and XGBoost-were employed to analyze the data.
RESULTS: The analysis revealed gender-specific patterns in SRH predictors. For men, education was the most critical factor, followed by diagnoses of physical and mental illnesses. For women, SRH was primarily influenced by chronic disease diagnoses, low education, and health insurance coverage. Alcohol consumption was a stronger predictor of poor SRH for men than women, likely due to cultural norms that promote higher alcohol use among men.
CONCLUSION: This study provides insights into midlife health disparities, emphasizing gender-specific factors influencing SRH. Machine learning demonstrated its value in uncovering nuanced patterns in health data, offering a powerful tool for public health research and policy.
OBJECTIVE: This study examines the importance of clinical and sociodemographic factors of SRH among middle-aged Brazilian adults and analyzes how they vary between men and women.
METHODS: A cross-sectional analysis was conducted using data from the 2019 National Health Survey (PNS) with a representative sample of 31,926 middle-aged adults (40-59 years) living in private households on Brazilian territory. Five machine learning models-Naive Bayes, SVM, Logistic Regression, Random Forests, and XGBoost-were employed to analyze the data.
RESULTS: The analysis revealed gender-specific patterns in SRH predictors. For men, education was the most critical factor, followed by diagnoses of physical and mental illnesses. For women, SRH was primarily influenced by chronic disease diagnoses, low education, and health insurance coverage. Alcohol consumption was a stronger predictor of poor SRH for men than women, likely due to cultural norms that promote higher alcohol use among men.
CONCLUSION: This study provides insights into midlife health disparities, emphasizing gender-specific factors influencing SRH. Machine learning demonstrated its value in uncovering nuanced patterns in health data, offering a powerful tool for public health research and policy.