Taking control of one’s health is essential for the prevention and timely treatment of medical conditions. Polycystic ovary syndrome is a very common health disorder among women, but due to its subtle symptoms, many women go undiagnosed. Dhruvi Kapadia, a 16- year- old student from John Dubai Nursery School, has created a community project that utilizes data analysis and image processing to detect the symptoms of PCOS and promote early diagnosis.
The program assesses a user’s BMI, weight changes, sleep habits, and menstrual cycle patterns to detect the risk of having PCOS. It also helps women spread awareness regarding the diagnosis and causes of the disorder. The program also takes into account the genetic and lifestyle factors that may lead to PCOS and alerts users if they have any of the common indicators.
The program also uses machine learning to detect the presence of acne and male pattern baldness, two common symptoms of PCOS. After assessing all the data, it produces an analysis that provides a user with an overview of their risk level.
The program provides a number of preventive measures to help reduce the risk of developing PCOS in the future. This includes exercising regularly, consuming herbal medicines like Shatavari, and having a healthy diet. The program also encourages users to take medical assistance if their risk level is high and detect the symptoms of PCOS before it can lead to more serious health complications.
