A Machine Learning Approach to Analyze the Drug Addiction

Effective therapies and solutions are required because drug addiction is a serious social problem. This study applies machine learning approaches to address several aspects of drug addiction. The primary goals include predicting the rate at which people become addicted to drugs, identifying drug types to which people are more vulnerable, assessing long-term addiction potential, developing an image processing-based method for identifying drug-addicted people, determining associated diseases and side effects, and predicting relapse after rehabilitation. This study intends to contribute to the improvement of precise, reliable, and tailored ways to combating drug addiction by using the power of machine learning algorithms. Predictive algorithms may assess the likelihood of addiction and identify high-risk individuals by analyzing large datasets that include demographic, social, and genetic characteristics. Here for Identifying the drug addiction people using image processing used CNN algorithm and accuracy is 91.09. Predicting the rate of a person becoming addicted to drugs, the type of the drug and whether a person can become addicted to drugs for a long-time component used Random Forest Classifier and accuracy is 90.89. Identify the diseases, side effects of drug users and to suggest treatments component we used Decision Tree Classifier and for that got accuracy as 90.16. Predicting whether or not a person will be addicted to drugs again after the rehabilitation process and Period of time to be rehabilitated again component used algorithm as Random Forest Classifier and accuracy is 96.75. This study aims to make substantial advances to the field of drug addiction by improving prevention, detection, and treatment efforts through the use of machine learning.