Machine Learning-Enhanced Solutions for Mitigating PCOS/PCOD-Related Women's Health Disparities in Rural Areas: A Holistic Telemedicine System Design and Implementation

Telemedicine plays a crucial role in modern healthcare by providing remote medical services that enhance access and equity, particularly in underserved areas. Polycystic ovary syndrome (PCDS) and polycystic ovarian disorder (PCOD) are prevalent reproductive health issues that significantly impact women globally. Access to gynecological care in remote regions is often hindered by a shortage of specialists, transportation challenges, cultural barriers, and low health literacy, resulting in delayed diagnoses and untreated conditions that can increase maternal and neonatal mortality rates. This paper presents a telemedicine initiative aimed at empowering women by offering essential gynecological services for PCDS and PCOD, regardless of geographical constraints. The platform includes remote video consultations, menstrual cycle tracking, educational resources, and appointment reminders, facilitated by Accredited Social Health Activist (ASHA) workers. These findings illustrate the effectiveness of this approach in improving healthcare access for women, promoting a more equitable future. Additionally, we introduce a machine learning model utilizing a Random Forest algorithm for patient classification based on PCDS and PCOD symptoms. Trained on an 80/20 split dataset, the model's accuracy is evaluated using confusion matrices. This study represents a significant step toward bridging geographical disparities in rural India and providing quality gynecological care.