DiaGuide: Diabetes Risk Prediction App
👋 Introduction
Hi there!
Last week, I built my first fully functional website, implementing AI to predict diabetes risk using historical data. This was my first time publishing a real, working website—and I am honestly proud of the result. And I built all this during 48-hour hackathon, working solo.
I used Streamlit for the UI, scikit-learn for the AI training, and a model. Here is how:
💡 The Idea
When the project topic was first released, I was slightly surprised to see healthcare 💓 — most of the hackathons I had joined previously allowed more open-ended, general tech solutions. But then I started thinking 🤔, and this pushed me to research more deeply.
Since I was good at data analysis and developing ML models, I decided to focus on those areas. I spent all of Friday and Saturday morning brainstorming ideas. I actually came up with a couple 🧠:
-
💊 Medication Tracker App
- An app to keep track of daily medication while allowing users to note how effective each medication is.
- ✅ Easy to implement
- ❌ The idea wasn't original enough
- ❌ I wasn’t sure how to keep track of the data on a web server
- ❌ I didn’t have enough time to learn and build a mobile app
-
❓Symptom-Based Doctor Recommendation
- A questionnaire to determine whether the person needs to go to the doctor.
- ✅ Useful in real life
- ❌ Too broad
- ❌ Hard to implement with all the parameters and questions
- ❌ Hard to find a reliable database to use
Because of the reasons I mentioned above, I decided to pivot. But I still liked the idea of using a questionnaire to determine something important.
So I shifted gears to something more specific — a disease or mental health condition. I was very indecisive at first, but then I found a very useful diabetes dataset, and the parameters made sense to me. That’s when I committed to building DiaGuide.
⚙️ Tools & Stack
🀄 Language(s): Python
💻 Frontend
- 🔨 Tools & Libraries: Streamlit
- ❓ Why I chose it: I needed a simple library that I could use to create layout and I needed it fast 🏃♂️💨. As someone who doesn't have much experience in frontend design, Streamlit helped me a lot with being beginner-friendly and having a good documentation 📖 and tutorials. It was the perfect tool to create data-heavy app that requires minimal UI design.
- 🛠️ How I used it: It was basically the cornerstone of my UI. Everything—from layout to interactivity—was built using 🐍 Python. No HTML, no CSS, just clean Python code.
🤖 Machine Learning
- 🔨 Tools & Libraries: scikit-learn
- 🔎 Model Type(s): Logistic Regression, Random Forest, Gradient Boosting
- 🧹 Data Cleaning Libraries: Pandas, Numpy
- 💪 Performance/Evaluation: I created 3 different models and evaluated each one of them by getting their ROC curve. This evaluation checks how successful the model is using the test sample when 1 = exact, 0.5 = same as randomly choosing:
- Logistic Regression: 0.81, very fast ⚡
- Gradient Boosting: 0.82, okay speed ⌛
- Random Forest: 0.77, slows down the code significantly 🐢 and slower server response
- 🏆 Result: Logistic Regression—it didn't have much accuracy difference with Gradient Boosting but was significantly faster.
📊 Dataset
- 🌐 Source: Kaggle / UCI Machine Learning Repository
- 🔗 Link: https://www.kaggle.com/datasets/alexteboul/diabetes-health-indicators-dataset/data
- 🔢 Features Used: BMI, Age, Blood Pressure, etc.
- 🤔 Why this dataset: ✅ Clean, 🏷️ labeled, 🧠 interpretable
- 📝 Additional Notes: I didn't use two of the columns in the database—education level and income—because I thought they were more personal. Also, I evaluated with and without them and they only increased the accuracy by 1%, which is not significant.
🌐 Hosting & Deployment
- 🚀 Where I deployed: Streamlit Cloud
- 🧑💻 GitHub Repository: SuleymanSade/DiaGuide
- 🌐 Streamlit App: DiaGuide App
- 🛠️ How I did it: I put all the libraries I used into
requirements.txt➡️ Configured Git LFS to fit my models (since some of them were bigger than 100MB) ➡️ Created a GitHub repo & pushed the code ➡️ Set up Streamlit Cloud and it was ready to go - ❓ Why I used Streamlit Cloud: It was free-to-use and really simple to set up. It also connected to the repository, so if I make a change in the future, the website is going to update too.
Top comments (0)