AN ENHANCED META-CLASSIFIER APPROACH FOR ALCOHOL ADDICTION PREDICTION

Abstract

Chronic alcohol consumption poses significant public health challenges globally. In underserved regions, the lack of AI-based interventions for alcohol addiction highlights a critical gap in the healthcare system, particularly regarding the early detection of alcohol abuse. Henceforth, this research aims to raise awareness of alcohol use disorder and proposes a novel AI-powered solution designed with an improved classification algorithm to address this deficiency, with a primary focus on a cutting-edge prediction model. This research shifts the current reactive approach in alcohol addiction intervention to proactive approach by employing an enhanced meta-classification algorithm (EMC) that focuses on improving the interpretability, efficiency, and accuracy of predictions. The proposed EMC ultimately provides a robust tool for healthcare professionals and patients which fosters more effective and personalized intervention strategies for alcohol addiction recovery. The results demonstrate a remarkable 10.13% improvement in balanced accuracy and a 9.72% enhancement in the area under the curve compared to traditional ensemble and state-of-the-art methods. Thus, findings from this study will assist medical practitioners and policymakers in developing evidence-based strategies to combat alcoholism and enhance public health outcomes. By deriving insights from real-world case study, the outcome of this research represents a pioneering effort to betterment of healthcare in underserved regions, offering a low-cost, scalable solution for early detection, and has the potential to significantly improve outcomes in marginalized communities.