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1
Anxiety is influenced by lifestyle, psychological, and demographic factors, with significant associations identified through a large survey of 11,000 adults.
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2
Machine learning algorithms, particularly ensemble models, outperformed traditional regression methods in predicting anxiety severity.
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3
Key predictors of anxiety include stress, sleep duration, and caffeine intake, highlighting the multifactorial nature of the condition.
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4
The study emphasizes the importance of integrating statistical methods with machine learning to enhance mental health care predictions.
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Future research should focus on longitudinal designs and incorporate biological and digital markers for improved anxiety prediction.
