A novel deep neural network approach to detect and monitor cocaine drug abuse

Cocaine is one of the most commonly used drugs that may lead to physical and mental health problems. It is necessary to identify individuals having cocaine use disorder as early as possible to monitor them properly. The objective of this work is to predict the time of cocaine use in scenarios where clinical testing is not possible. The time of cocaine use is defined as how many days before the individual has used cocaine.

It is possible to predict the time of cocaine use based on personality traits and demographic information as features. The personality traits (neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness, impulsivity, and sensation seeking) along with demographic information features (education level, age, gender, country of residence, and ethnicity) have been used to predict the time of cocaine use. These features are given as inputs to long short-term memory networks (LSTM) to predict the time of cocaine use.

The highest F-score for the prediction of time of cocaine use for the LSTM method is found to be 0.99. A comparative study has also been carried out using both deep neural networks and artificial neural networks to predict the time of cocaine use to demonstrate the superiority of the LSTM method. The proposed method shows promising results for predicting the time of cocaine use and can be considered for monitoring the cocaine use disorder.

The proposed method will be an efficient tool to identify the mental health of a person if the person has cocaine use disorder. As a result, proper treatment can be given to the individual in time.

The originality of the work is that it predicts the time of cocaine use with better accuracy. The LSTM method has not been used previously for predicting the time of cocaine use.