I built ML-Diabetes-Prediction-API as a machine-learning backend using FastAPI and Docker. It deploys an AdaBoost classification model to predict diabetes risk from medical parameters. The README titles it “Diabetes Prediction API (FastAPI + AdaBoost).” My GitHub profile also lists this project among my applied ML work in github profile.
I describe the request flow as a FastAPI POST endpoint at /predict, with Streamlit or Postman as clients. Pydantic validates input data types and value ranges before feature engineering. I compute BMI_Age = BMI × Age and Glucose_BMI = Glucose × BMI, then convert the input into a Pandas DataFrame compatible with the model.
The API loads a .pkl AdaBoost binary-classification model. It uses predict() for the class label and predict_proba() for the probability score, then returns both in a JSON response. The target mapping is 0 for Not Diabetic and 1 for Diabetic.
{
"prediction": "Diabetic",
"diabetes_probability": 78.43
}
The repository is on my GitHub account, @Aakashkumar017. Its listed metadata describes it as a Jupyter Notebook repository with 1 star and the topics ai, fastapi, and ml; it was started and last updated in 2026-02. The README calls the backend production-ready and Docker-built, but the excerpt does not give Docker build or run commands or deployment configuration.
The available README excerpt does not provide model evaluation metrics, training-data details, the full input schema, validation ranges, or evidence of actual model performance. It also does not clarify whether the example probability value 78.43 is formatted as a percentage. The documentation shows http://<redacted>:8000/docs, so I do not have the actual host.