Revolutionizing PCOD Detection with Early Machine Learning Methods

Abstract

Polycystic Ovarian Disease (PCOD) poses significant challenges in early detection due to its diverse symptomatology and the absence of accessible, real-time diagnostic tools. A compact, intelligent system has been developed to enable non-invasive monitoring of physiological and hormonal indicators associated with early-stage PCOD. The architecture integrates embedded computing with a multimodal sensor array to collect data related to cardiovascular activity, hormonal fluctuations, body temperature, and bioelectrical signals. Real-time signal acquisition and preprocessing are handled through an edge-computing platform with wireless communication capabilities. Machine learning algorithms are employed to analyze temporal patterns and anomalies in the acquired data, enabling the identification of early markers linked to PCOD. Preliminary validation demonstrates the potential of the system to support low-cost, portable, and proactive screening, thereby contributing to improved diagnostic timelines and enhanced outcomes in women’s reproductive health.