Precision Care in Addiction Treatment: A Bayesian-Based Machine Learning Analysis for Adults with Substance Use Disorders

The harmful consumption of substances like alcohol, cannabis, cocaine, or heroin is commonly termed as drug addiction. It can result in a specific dependence, either physical or mental. The idea of addiction to substance use is rooted in a series of behaviours or patterns linked to the utilization of the specific substance. Here the Naïve Bayes model is used to examining the addict’s activities. Internal health professionals and addictologists employ a variety of factors, such as an individual’s environments and family ties, to make predictions about a person’s substance addiction. This strategy, however, is not straightforward and necessitates an analysis of previous patients’ behaviour while taking into account frequent medicine consumption. Modern addiction treatment recognizes the need for personalized approaches for those with Substance Use Disorders (SUDs). Traditional methods may not address individual challenges effectively. Recent advancements, using Bayesian-based ML, aim to analyse patient data more precisely. This allows for tailored treatment plans, predicting outcomes and adapting interventions. This approach acknowledges the variations in SUD responses and empowers clinicians to provide more targeted and effective care, shaping the future of addiction treatment. The predicted model is based on a wide range of characteristics, similar to previous cases of acquitted substance addicts’ significant reliance and failures in their personal lives. The effectiveness of this classifier was assessed using a Naïve Bayes ML technique. Common metrics in ML are employed to evaluate the efficacy of the constructed model which includes Discover rate, False Detection Frequency, Accurateness, Definitiveness and Testing Measure. It is evident that employing ML – based models to forecast persons susceptible to substance abuse can be highly advantageous for drug users. The Bayesian method engendered a delicacy score of 92.85 out of 100.