A patient-centric machine learning-based phone application for predicting the risk of polycystic ovarian syndrome

Polycystic ovarian syndrome (PCOS) is a common endocrine disease in women with multisystem organ involvement with implications on cardiovascular health and fertility. Insufficient health information, diagnostic delays, and suboptimal interventions lead to greater morbidity. To mitigate this disease burden, a machine learning algorithm was created and integrated into a patient-centric phone application that predicted a patient's risk of PCOS based on common demographic and biochemical markers along with appropriate recommendations on suggested interventions. The application also had an aspect of patient education to address knowledge gaps in the community. The phone application was successfully deployed internationally and is free to download on major platforms to ensure equitable access.