Optimizing Telemedicine Services for Rural Women: A Decision Tree-Based Heap Optimizer Approach for Predictive Healthcare Delivery

difficulty gaining access to healthcare especially in
underdeveloped areas where the infrastructure for medical
treatment is limited. By means of remotely available healthcare
services, telemedicine has become a possibly useful solution to
close this gap; still, the optimisation of these services to generate
better outcomes is a continuous challenge. Even if telemedicine
is becoming more and more popular, women living in rural
areas still sometimes face challenges including limited health
resources, socioeconomic problems, and poor internet access.
Therefore, much sought for are effective optimisation models
that can predict the needs of healthcare and enhance the
provision of services to this particular group. This work
attempts to provide a Decision Tree-based Heap Optimiser
(DTHO) for use in rural women's telemedicine service
prediction and optimisation. Using decision tree learning, the
DTHO method classifies healthcare needs depending on past
data. Moreover, used are heap-based approaches for effective
resource distribution. Using data from surveys on telemedicine
use and rural healthcare, the predictive model was developed by
means of analysis. The model maximised resource allocation by
30%, so allowing a more efficient provision of telemedicine
services. For identifying healthcare needs, it also attained an
accuracy of 85%. Reducing service delays by 20-25 percent
enabled the heap-based optimiser to increase patient
satisfaction by 20%.