A machine learning web application built using Streamlit that predicts whether or not a patient has diabetes considering multiple health parameters such as Blood Pressure, Insulin & Glucose Level, BMI (Body Mass Index), Age, Pregnancies etc.
The trained dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has diabetes. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. The dataset can be found on Kaggle. It includes following health criteria:
- Pregnancies: Number of times pregnant
- Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test
- BloodPressure: Diastolic blood pressure (mm Hg)
- SkinThickness: Triceps skin fold thickness (mm)
- Insulin: 2-Hour serum insulin (mu U/ml)
- BMI: Body mass index (weight in kg/(height in m)^2)
- DiabetesPedigreeFunction: Diabetes pedigree function
- Age: Age (years)
- Outcome: Class variable (0 or 1)
streamlit scikit-learn
numpy pandas matplotlib seaborn
Clone the repository and install the required dependencies using the following commands:
git clone https://github.com/aman-chhetri/Diabetes-Prediction-App.gitcd Diabetes-Prediction-Apppip install -r requirements.txtstreamlit run app.py- Open the app in your web browser.
- Enter the required information in the input fields.
- Click the
Diabetes Test Resultbutton to generate the prediction.
This tool has been deployed using Streamlit. Learn about streamlit deployment here. Checkout the notebook repository here from where the pickle file has been imployed in the tool.
If you have any questions or feedback, feel free to reach out 🙂
- Email: [email protected]
- LinkedIn : @amankshetri
- Twitter : @iamamanchhetri