Exploring Cyberchondria among Young Adults: An Iceberg
DOI:
https://doi.org/10.48047/cf3jra50Keywords:
Cyberchondria, Deep Q Network, Quantile Normalization, Accuracy, Over samplingAbstract
Cyberchondria behavior, characterized by excessive online health searching, has become a
growing concern in the digital age. This behavior can lead to increased health anxiety, decreased
quality of life, and unnecessary healthcare utilization. However, detecting cyberchondria behavior
is a challenging task due to the complexity and variability of online behaviors. Traditional machine
learning approaches often rely on hand-crafted features and may not capture the nuanced patterns
of cyberchondria behavior. To address this challenge, a novel approach using Deep Q Network
(DQN) is proposed to detect cyberchondria behavior
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