The problem Health Visor solves

In today's fast-paced world, people often prioritize other aspects of life over their health. Unfortunately, ignoring potential symptoms can lead to significant health issues down the line. According to Google, India witnesses a staggering number of cancer-related deaths, with breast cancer accounting for approximately 1 lakh deaths, brain tumors for over 25,000 deaths, and skin cancer for nearly 10 lakh cases. One of the primary causes of skin cancer is excessive exposure to harmful UV rays, which is a significant concern in India's scorching summers.

To address these challenges, we introduce Health Visor, a platform that empowers individuals to monitor their health conveniently. By simply entering their symptoms, users can access an initial assessment of their health status. Furthermore, Health Visor enables users to book appointments with doctors who can review their information and provide guidance. We have also developed photo detection models that assist in identifying brain tumors and pneumonia. For brain tumor detection, users can upload their brain MRI scans, while X-rays are required for pneumonia detection.

Our platform includes an app that tracks users' live locations and provides real-time updates on the UV index in their area. Additionally, we maintain a location timeline to help users understand their cumulative exposure to UV rays. Health Visor offers valuable suggestions and precautions to help users protect themselves from harmful UV radiation.

In the realm of breast cancer detection, we have trained a machine learning model. This model takes numerical inputs from users in tabular format and provides them with accurate results.

With Health Visor, we aim to bridge the gap between individuals' busy lives and their health needs. By offering accessible and comprehensive health monitoring solutions, we strive to improve early detection, timely intervention, and ultimately, enhance overall well-being.

Challenges we ran into

We encountered various problems when developing our product within the time constraints. These challenges might be classified as technical and data-related issues. Some of these issues were caused by a lack of records and databases, as well as user comments. We also had issues installing libraries that were not supported by our system, which necessitated troubleshooting and the discovery of other alternatives. Furthermore, maintaining the accuracy of our machine learning models proved difficult, needing ongoing modification and optimisation. Another critical factor that required thorough study and decision-making was ensuring that we employed the suitable tech stack to meet our goals. To develop a robust and effective solution, these obstacles required a combination of problem-solving skills, resourcefulness, and perseverance.