PawPulse: Diabetes Monitor

Here’s how you can structure these sections for PawPulse:

Inspiration

We were inspired by the growing prevalence of diabetes and the importance of early detection to prevent complications. Our goal was to use machine learning to predict diabetes risk based on various health factors. We also wanted to build a tool that is simple, accessible, and impactful for everyday use, drawing from data-driven insights to help individuals take control of their health.

What it does

PawPulse analyzes user-input health data, such as BMI, blood pressure, lifestyle habits, and more, and uses machine learning models to predict the likelihood of diabetes. It compares predictions from different datasets, providing users with personalized, accurate results and tips on managing their health risks. The tool is designed to be interactive and easy to use, making it accessible to individuals and healthcare providers alike.

How we built it

We used four datasets that provide various health metrics related to diabetes. Using Python and popular libraries like Scikit-learn, TensorFlow, and Pandas, we trained machine learning models on each dataset. Our focus was on ensuring that the data was standardized for accurate comparisons. The project was built using a Raspberry Pi, which allows for a physical presence, evoking the tangible tech culture of the early 2000s, while also integrating with modern healthcare technologies.

Challenges we ran into

Standardizing the data across four different datasets was a major challenge. Each dataset had different variables and structures, so we had to carefully clean, normalize, and preprocess the data to ensure accurate model comparisons. Time constraints also posed challenges, particularly in making the project interactive and engaging for users within the limited hours of the hackathon.

Accomplishments that we're proud of

We’re proud of successfully integrating and comparing predictions from multiple datasets, as well as creating an intuitive, user-friendly interface that provides clear, actionable health insights. Additionally, implementing the project on a Raspberry Pi added an extra layer of physical interaction that sets our project apart.

What we learned

We gained valuable experience in data preprocessing, especially when working with multiple datasets. We also deepened our understanding of machine learning models for healthcare applications, and learned how to optimize them for accuracy. Moreover, we learned how to deliver a project with both technical complexity and user engagement in a short period of time.

What's next for PawPulse

The next step for PawPulse is to improve the accuracy of our models by incorporating even more diverse datasets and refining the machine learning algorithms. We also plan to add more interactive features, such as real-time health tracking, and explore partnerships with healthcare providers to make PawPulse available to a wider audience. Additionally, we aim to develop a mobile-friendly interface and expand the predictive features to include other health conditions.