Drug Addiction Prediction Using Machine Learning

Abstract

Drugs and alcohol are dangerous to health and the body. Nowadays drug addiction has become a threat to Bangladeshi young people. Drugs and alcohol have a negative impact on our life. We have to keep an eye on the young people of our country not getting addicted to drugs quickly. We need to stay away from the drug before getting addicted to it. We will predict the risk of becoming addicted to drugs with machine learning. First, we study some related papers, journals, and online articles then we talk to doctors and drug addicts people; we find some common factors related to becoming addicted to drugs. Then we collect data based on those factors, such as age, gender, profession, health ability, mental pressure, trauma, family and friend’s history, incidents, etc. We collect data from both addicted and non-addicted people. We have two outcomes. One is ‘Yes’ means addicted and another is ‘No’ means not addicted. After data collection, we processed all the data and created a processed dataset. We applied machine-learning algorithms to our processed dataset. Since machine learning, artificial intelligence and deep learning used in various predictions and detection systems. We use k-nearest neighbor (kNN), logistic regression, support vector machine (SVM), naïve Bayes, random forest, adaptive boosting (ADA boosting), decision tree, multilayer perceptron (MLP) and gradient boosting classifier. In our work, out of nine algorithms, logistics regression gave the best performance based on accuracy and the accuracy of logistic regression was 97.91%.