2 Years Impact Factor:
2.85
5 Years Impact Factor: 1.53
Author: Prashanth Oddepally , Thata Akshitha , Gayathri Koppy,Mr. Kranthi Kumar
Abstract:
The global telemedicine market is expected to reach $559.52 billion by 2027, with healthcare costs rising at an estimated rate of 5.5% annually. As telemedicine becomes an integral part of modern healthcare, accurately forecasting future costs is crucial for optimizing healthcare expenditures and resource allocation. Existing cost estimation models often lack precision, failing to account for the dynamic nature of healthcare expenses and the conditions under which telemedicine provides a cost-effective alternative. This study introduces a deep learning-based approach for estimating future healthcare costs associated with telemedicine services. The model employs a regression-based cost prediction framework while simultaneously classifying scenarios where telemedicine is a viable solution. The dataset undergoes comprehensive preprocessing, including data normalization, handling of missing values, and feature engineering, to improve model robustness and predictive accuracy. Advance
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