A Systematic Literature Review on Implementation of Hybrid Machine Learning-Based Predictive Analysis Model for Customer Use of a Mobile Fitness Application

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Published: Feb 1, 2025
DOI: https://doi.org/10.29070/ksk7by60
Keywords:
Machine learning, Fitness applications, health, physical activity, customer usage

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Authors

Shweta Sharma

Research Scholar, ICFAI Tech, ICFAI University, Jaipur, Rajasthan

Dr. A. K. Saini

Director, ICFAI Tech, ICFAI University, Jaipur ,Rajasthan

Abstract

Access to health and fitness-related services is now more convenient thanks to the rising popularity of mobile fitness applications. Mobile fitness apps can employ predictive analytics to personalize user experiences to increase user engagement and retention. Hybrid machine learning models have been popular in recent years as efficient methods for predictive analysis across a range of fields. This comprehensive evaluation of the literature intends to investigate the usage of hybrid machine learning-based predictive analysis models specifically for user interaction with mobile fitness applications. We will talk about the advantages of the Hybrid Machine Learning-Based Predictive Analysis Model for Customer Use of a Mobile Fitness Application. The review summarizes the results from numerous research that were published between 2018 and 2022. In this study, 50 publications were analyzed to identify the research gaps relevant to the learning-based predictive analysis model for customer use of a Mobile Fitness Application. The findings of this review give academics and practitioners in the field of mobile fitness applications useful insights into the current state-of-the-art & future directions.

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Issue

Vol. 20 No. 1 (2025): International Journal of Information Technology and Management (IJITM)

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