https://github.com/nafisalawalidris/building-a-linear-regression-model-to-predict-diabetes-progression

Predict Diabetes Progression: A linear regression model to predict diabetes progression using patient attributes. Collaborate to improve predictions. Jupyter Notebook implementation.
https://github.com/nafisalawalidris/building-a-linear-regression-model-to-predict-diabetes-progression

collaborative-development data-science diabetes-progression jupyter-notebook linear-regression machine-learning medical-data-analysis predictive-modeling

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Predict Diabetes Progression: A linear regression model to predict diabetes progression using patient attributes. Collaborate to improve predictions. Jupyter Notebook implementation.

• Host: GitHub
• URL: https://github.com/nafisalawalidris/building-a-linear-regression-model-to-predict-diabetes-progression
• Owner: nafisalawalidris
• Created: 2023-07-22T22:55:28.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-07-22T23:01:56.000Z (over 1 year ago)
• Last Synced: 2024-11-22T21:16:37.560Z (2 months ago)
• Topics: collaborative-development, data-science, diabetes-progression, jupyter-notebook, linear-regression, machine-learning, medical-data-analysis, predictive-modeling
• Language: Python
• Homepage:
• Size: 4.88 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        


  
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

  


    
Load and Explore the Data: We will load the dataset and explore its contents to gain insights into the data.

    
Data Preprocessing: Perform necessary data preprocessing steps such as handling missing values, encoding

      categorical variables, and scaling numeric features.

    
Correlation Analysis: Examine the correlation between each feature and the target variable to identify relevant

      features for the model.

    
Feature Selection: Select the features that have a strong correlation with the target variable for training the

      model.

    
Split the Data: Divide the dataset into training and testing sets to evaluate the model's performance.

    
Build the Linear Regression Model: Create a linear regression model using the selected features and train it on

      the training data.

    
Make Predictions: Use the trained model to make predictions on the test data.

    
Evaluate the Model: Calculate the Mean Squared Error (MSE) to measure the performance of the model.

    
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.