Building a Linear Regression Model to Predict Diabetes Progression

In this project, we will be building a linear regression model to predict the progression of diabetes in patients based on various medical attributes. We have an anonymized dataset of past diabetic patients, and our goal is to create a model that can help the doctors at Greene City Physicians Group (GCPG) predict the disease progression in patients.

Data File

The dataset is available in the Jupyter Notebook file LinearRegression.ipynb. It contains the following attributes:

• age: The patient's age in years.
• sex: The patient's sex.
• bmi: The patient's body mass index (BMI).
• bp: The patient's average blood pressure.
• s1-s6: Six different blood serum measurements taken from the patient.
• target: A measurement of the disease's progression one year after a baseline.

Scenario

GCPG is a medical practice that provides treatment in various fields, including endocrinology. The endocrinologists at GCPG treat hundreds of different diabetic patients, helping them manage the disease. Preventing the disease from reaching more severe stages is crucial, and the doctors are interested in predicting when a patient is at risk of progressing to later stages.

Approach

Since the target variable (target) is an ordinal numeric value, we will create a linear regression model to predict the disease progression. Linear regression is a suitable technique for predicting continuous numeric values.

Steps Involved

1. Load and Explore the Data: We will load the dataset and explore its contents to gain insights into the data.
2. Data Preprocessing: Perform necessary data preprocessing steps such as handling missing values, encoding categorical variables, and scaling numeric features.
3. Correlation Analysis: Examine the correlation between each feature and the target variable to identify relevant features for the model.
4. Feature Selection: Select the features that have a strong correlation with the target variable for training the model.
5. Split the Data: Divide the dataset into training and testing sets to evaluate the model's performance.
6. Build the Linear Regression Model: Create a linear regression model using the selected features and train it on the training data.
7. Make Predictions: Use the trained model to make predictions on the test data.
8. Evaluate the Model: Calculate the Mean Squared Error (MSE) to measure the performance of the model.
9. Visualize Results: Plot lines of best fit for the features that have the strongest correlation with disease progression.

Conclusion

By building a linear regression model, we can help the endocrinologists at GCPG predict the progression of diabetes in their patients. With accurate predictions, early interventions can be provided to prevent the disease from reaching more severe stages, improving patient outcomes and overall healthcare management.

Results

The linear regression model successfully predicts the disease progression in diabetic patients. The Mean Squared Error (MSE) was calculated to measure the model's performance, and the results indicate its effectiveness in predicting the target variable.