Diabetes Prediction Machine Learning Project Using Python

Introduction—

MATLABSolutions demonstrate In this task we are going to design the project. According to WHO, Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces. Insulin is a hormone that regulates blood sugar. Hyperglycaemia, or raised blood sugar, is a common effect of uncontrolled diabetes and over time leads to serious damage to many of the body's systems, especially the nerves and blood vessels

Between 2000 and 2016, there was a 5% increase in premature mortality rates (i.e. before the age of 70) from diabetes. In high-income countries the premature mortality rate due to diabetes decreased from 2000 to 2010 but then increased in 2010-2016. In lower-middle-income countries, the premature mortality rate due to diabetes increased across both periods.

Diabetes Prediction Machine Learning Project using Python Streamlit

Below are the steps by which we can make a Diabetes Prediction Machine Learning Project using Python Streamlit:

Step 1: Create a Virtual Environment

                         virtualenv env 
                         .\env\Scripts\activate.ps1

Step 2: Install Libraries

First, install all the essential libraries using the following commands. In a Diabetes prediction GUI built with Streamlit, these libraries serve critical roles. Numpy enables efficient data handling, Pickle5 is essential for saving and loading machine learning models, and Streamlit empowers the creation of simple web apps from data analysis scripts, making them accessible to users without the need for advanced web development skills.

pip install numpy
                              pip install pickle5
                             pip install streamlit

Step 3 : File Structure

Open File Structure

Step 4: Writing Our Code

After installing all the libraries we write our coding part :

• Model Loading: The code loads a pre-trained machine learning model from a file named ‘trained_model.sav‘ using the pickle.load method. This model is stored in the variable loaded_model and will be used for making predictions.
• Diabetes Prediction Function: The diabetes_prediction function is defined to make predictions based on user input. It takes a list of input data, converts it to a NumPy array, reshapes it, and then uses the loaded model to make a prediction. If the prediction is 0, it returns ‘Non Diabetic,’ otherwise, it returns ‘Diabetic’.
• Main Function: The main function is the core of the web application. It uses Streamlit to create a simple web interface for diabetes prediction. It displays input fields for various features related to diabetes, such as number of pregnancies, glucose level, blood pressure, and more. After entering the data, the user can click a “Predict” button to trigger the prediction.
• User Interface: The code defines the layout of the web app, takes user input for the diabetes-related features, calls the diabetes_prediction function with the user’s input, and displays the prediction result using the st.success function. If the user clicks the “Predict” button, the result is shown, indicating whether the individual is predicted to be diabetic or non-diabetic.