Our web app is designed to address the cultural taboos and financial barriers surrounding gynecological health in India. With high consultation fees and societal hesitations, women often neglect routine checkups and avoid discussing sensitive topics like contraceptives and STIs. Our app aims to provide a comprehensive and convenient solution, acting as a one-stop platform for gynecology-related information and services.
The app's primary features include an assessment tool for fertility factors, personalized advice on optimizing fertility, and recommendations for suitable treatments or referrals to fertility specialists when necessary. We offer counseling and guidance on various contraceptive methods, helping individuals choose the most appropriate option based on their lifestyle, health history, and preferences. It will all be backed by certified doctors and all the tools will take help of machine learning models that use real-world dataset . Additionally, our app utilizes advanced machine learning models to detect STIs/STDs, enabling users to assess their risk and take appropriate actions.
From a gynecologist's perspective, the app allows patients to ask questions and utilize assessment tools before consultations. This streamlines the process and provides the gynecologist with comprehensive information during the appointment. By obtaining user consent, gynecologists can access the user's medical gynecological data stored securely over their lifetime using blockchain technology and zero-knowledge proof.
From a business standpoint, our app can establish partnerships with companies that sell medicines and contraceptives, creating a marketplace for users to conveniently access these products. This not only enhances the app's functionality but also provides a potential revenue stream.
We aim to empower women to prioritize their well-being and make informed decisions about their reproductive health.
We really struggled with finding the datasets for our machine learning models - STI detection, contraceptive recommendation. Since the topic we chose is a taboo, it's dataset is also not publically available. Also, building even the most basics of the machine learning models was hard for us because we have just stated with machine learning.