A Machine Learning Framework for Digital Game Addiction Detection and Early Intervention

Project Code :TCMAPY2333

Objective

This project aims to develop a machine learning-based framework for detecting digital game addiction by analyzing factors such as gaming behavior, health, academic performance, mood, and social interactions. The system will classify individuals into risk levels and provide early intervention recommendations through a user-friendly web interface. The goal is to enable timely interventions, mitigate the negative impacts of excessive gaming, and contribute to understanding digital game addiction.

Abstract

This project aims to develop a machine learning-based framework to identify individuals at risk of digital game addiction. The dataset used in this project contains several features, including gaming behavior, physical and mental health indicators, academic performance, and social interactions, to predict the likelihood of addiction. The system employs various machine learning algorithms to analyze the relationships between gaming habits and other aspects of life. By detecting individuals at risk of gaming addiction, the system helps in offering timely interventions to reduce negative outcomes. The framework uses a web-based application with front-end technologies such as HTML, CSS, and JavaScript, while the back-end is powered by Python with Flask. The project aims to contribute to the understanding of digital game addiction and the development of solutions for its prevention.

Keywords: Digital Game Addiction, Machine Learning, Early Intervention, Gaming Behavior, Physical Health, Academic Performance, Addiction Risk, Web Application, Flask, Prediction

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.