Cocaine addiction is a global public health crisis with significant costs. Current diagnostic methods are subjective and lack predictive value. Leveraging machine learning, this study aims to use demographic, personality, and drug use data to enhance predictions of cocaine addiction risk. Various ML algorithms will be compared, and features optimized to develop accurate models for targeted prevention and early intervention in high-risk populations, offering hope for improved outcomes.
