International Journal of Science, Engineering and TechnologyAuthors: Tanish Aggarwal, Sunil.K.Singh, Tamanna Aggarwal, Amit Chhabra, Jagmohan Aggarwal
Abstract: Patient demographics, including gender and age, play an important role in determining access to health services and options. This study examines these demographic differences across health care systems, revealing significant differences in patient engagement and service use. With the rise of machine learning, the optimization of telemedicine has emerged as a promising strategy to improve patient care. This paper explores the use of advanced machine learning algorithms to improve telemedicine by enhancing predictive capabilities and patient engagement. Patient behavior and need patterns can be identified through predictive analytics, leading to optimized scheduling and resource allocation, increasing telemedicine utilization In- corporating machine learning into telemedicine processes does not seem to provide not only improves patient communication but also provides higher quality healthcare -emphasizes memory efficiency . By exploring these innovations, the study highlights the trans-formative potential of machine learning in telemedicine, paving the way for future advancements in digital health through increased accessibility, predictive analytics, automated reminders and data-driven insights, ultimately contributing to better patient outcomes.
DOI: https://doi.org/10.5281/zenodo.20083908
