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All Journal Enthusiastic : International Journal of Applied Statistics and Data Science
Han, Lim Whei
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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AI-Based Personalized Virtual Therapist for Alcohol Relapse Win, Myat Noe; Han, Lim Whei; Samson Chandresh Kumar, Eric Kamalesh Kumar; Keat, Tan Yong; Ravana, Sri Devi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 2, October 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss2.art3

Abstract

Binge drinking is one type of harmful alcohol use that has a variety of negative health impacts in both the drinker and others, either globally or in Malaysia. According to previous research, one in two current drinkers in Malaysia who are 13 years and older reported having engaged in binge drinking. Therefore, increased attention should be given to understand the drinking pattern of an individual and propose a solution that can help with addiction relapse. Thus, this study identified interventions that could assist alcohol relapse recovery and proposed a new generation of relapse prevention solution based on artificial intelligence (AI). By using a deep learning approach and machine learning based recommendation technique, it predicts the relapse rate of users, providing recovery consultation based on the user’s data and clinical data through a chatbot. This study involved helpful data collection, advanced data modeling, prediction analysis to support the alcohol relapse recovery journey. Hence, the proposed AI solution acted as a personalized virtual therapist to help the addicts stay sober. The objective is to present the design and realization of the AI based solution for sober journey. The proposed solution was tested with pilot study and significant benefits of virtual therapists for alcohol addiction relapse is reported in this paper.
Co-Authors Keat, Tan Yong Ravana, Sri Devi Samson Chandresh Kumar, Eric Kamalesh Kumar Win, Myat Noe