  The information ecosystem today is overwhelmed by an unprecedented quantity
of data on versatile topics are with varied quality. However, the quality of
information disseminated in the field of medicine has been questioned as the
negative health consequences of health misinformation can be life-threatening.
There is currently no generic automated tool for evaluating the quality of
online health information spanned over a broad range. To address this gap, in
this paper, we applied a data mining approach to automatically assess the
quality of online health articles based on 10 quality criteria. We have
prepared a labeled dataset with 53012 features and applied different feature
selection methods to identify the best feature subset with which our trained
classifier achieved an accuracy of 84%-90% varied over 10 criteria. Our
semantic analysis of features shows the underpinning associations between the
selected features & assessment criteria and further rationalize our assessment
approach. Our findings will help in identifying high-quality health articles
and thus aiding users in shaping their opinion to make the right choice while
picking health-related help from online.
