Enterprise AI Analysis
Factors Associated with COVID-19 Mortality in Mexico: A Machine Learning Approach Using Clinical, Socioeconomic, and Environmental Data
This study employed machine learning models, specifically XGBoost, on a comprehensive national dataset from Mexico to identify clinical, socioeconomic, and environmental factors influencing COVID-19 mortality. The models achieved high predictive performance, highlighting key risk factors such as older age (50+), pneumonia, intubation, and comorbidities like diabetes, hypertension, and chronic kidney disease. Protective factors included younger age, outpatient status, and higher socioeconomic levels. Importantly, water quality contaminants also emerged as significant influencing variables, suggesting a complex interplay of factors.
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0.97
Predictive Performance (F1 Score)
0.94
Model Consistency (MCC)
7+
Key Risk Factors Identified
10M+
Data Points Analyzed
Deep Analysis & Enterprise Applications
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Model Performance
Clinical & Demographic Factors
Socioeconomic & Environmental Influences
XGBoost models consistently achieved high predictive performance across all datasets, with average F1 scores exceeding 0.97 and MCC values above 0.94. This robust performance indicates their utility in identifying individuals at increased risk of death. Hyperparameter tuning using grid search and cross-validation ensured optimal model configurations.
Older age (especially 50+ years), pneumonia, and intubation were identified as critical risk factors, corroborating previous research. Diabetes, hypertension, and chronic kidney disease also emerged as significant comorbidities. Protective factors included younger age groups (0-39 years old) and outpatient status. Female sex showed a modest protective effect.
Very high levels of the Human Development Index (HDI) and its health (HS) and income (IS) subindexes were protective against mortality, while lower levels were associated with increased risk. Intriguingly, water quality contaminants (e.g., manganese, hardness, fluoride, dissolved oxygen, fecal coliforms) ranked among the top 30 features, suggesting a potential link between environmental exposure and COVID-19 outcomes, warranting further investigation.
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Results Analysis
0.97
Average F1 Score across all models, highlighting robust predictive power.
| Factor Type | Risk Factors | Protective Factors |
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| Clinical |
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| Comorbidities |
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| Demographic |
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| Socioeconomic |
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| Environmental |
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Precision Mortality Prediction for Enhanced Resource Allocation
By accurately identifying high-risk patients using clinical, socioeconomic, and environmental data, healthcare systems can optimize resource allocation, prioritize interventions, and implement targeted public health strategies. This precision approach significantly reduces mortality and improves patient outcomes, especially in vulnerable populations.
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