Detecting Nicotine Addiction via Eye Tracking
Research Project | 2016-18 | Prof. Rahul Garg
Smoking addiction is a growing social epidemic affecting about 1 in 5 adults in the world directly and many more in the form of passive smoking. Diagnostic tools to determine the nicotine addiction are either unreliable or restrictive in their use. This makes identification and assessment less accessible for those who need it. We attemptedto evaluate eye tracking paired with machine learning as an alternative to existing diagnostic tools for assessing nicotine dependence.
We were able to achieve a classification accuracy of 83.33% when using features from all tasks. We were also able to predict the FTND score with a normalized mean absolute error of 0.73 using simple linear regression with an R2value of 0.38 and p-values of the intercept and coefficient as 0.046 and 0.0003 respectively
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