Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 11 2 2025 06 15 197 213 Esin AVCI Giresun University 2025 01 09 2025 03 03 Abstract Background Uncertainty in human life often arises from a lack of knowledge based on past events or unrealized circumstances. The Naive Bayes classification technique, rooted in conditional probability, offers a hypothesis-driven approach to linking two random occurrences and calculating posterior probabilities. Substance addiction remains a critical issue, particularly in patients hospitalized in community mental health centers, necessitating effective predictive methods for early identification and intervention. Methods This study employed the Naive Bayes algorithm to classify substance addiction tendencies in patients. To enhance prediction accuracy, feature selection was conducted using the Information Value (IV) method. Ten patient attributes were analyzed, including gender, education level, income status, and relationship status with family and environment, among others. Features with strong or medium predictive power were prioritized for the model. Results Four features—gender, education level, income status, and relationship status with family and environment—demonstrated strong or medium predictive power for substance abuse. The Naive Bayes algorithm revealed that males are approximately four times more likely than females to develop substance addiction. Patients with education levels ranging from primary to high school were more prone than those with college-level education or higher. Additionally, those under state protection exhibited a higher likelihood of substance abuse compared to other income statuses. Finally, individuals with poor or neutral relationships with family and their environment were more susceptible to addiction. Conclusion The Naive Bayes algorithm effectively classified substance addiction tendencies in hospitalized patients, emphasizing key predictive factors such as gender, education level, income status, and relational dynamics. These findings highlight the importance of targeted interventions tailored to at-risk populations, improving early detection and management strategies in community mental health settings. https://jbe.tums.ac.ir/index.php/jbe/article/view/1566