https://github.com/birmingham-and-solihull-ics/unsupervised-clustering-practices

K-means and hierarchical clustering of GP practices based on QOF performance measures
https://github.com/birmingham-and-solihull-ics/unsupervised-clustering-practices

hierachical-clustering k-means-clustering python qof unsupervised-machine-learning

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K-means and hierarchical clustering of GP practices based on QOF performance measures

• Host: GitHub
• URL: https://github.com/birmingham-and-solihull-ics/unsupervised-clustering-practices
• Owner: Birmingham-and-Solihull-ICS
• License: other
• Created: 2025-02-20T10:54:09.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-02-20T10:57:43.000Z (4 months ago)
• Last Synced: 2025-03-11T09:44:32.898Z (4 months ago)
• Topics: hierachical-clustering, k-means-clustering, python, qof, unsupervised-machine-learning
• Homepage:
• Size: 24.4 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# **GP Practice Segmentation Using Unsupervised Machine Learning**

## **Project Overview**

This project aims to apply **unsupervised machine learning** techniques, specifically **K-Means clustering** and **Hierarchical clustering**, to segment **General Practitioner (GP) practices** based on **Quality and Outcomes Framework (QOF) performance measures** and other **demographic markers**. By identifying meaningful clusters, this analysis will help uncover patterns in healthcare performance and inform targeted interventions.

## **Objectives**

- Explore and preprocess **QOF performance data** and **demographic indicators**.

- Apply **K-Means** and **Hierarchical clustering** to group GP practices into meaningful segments.

- Evaluate the clustering results to identify patterns and insights.

- Visualize and interpret the clusters for actionable recommendations.

## **Project Status**

🟢 **Ongoing** (Early Stages)  

- **Data collection**: In progress  

- **Data cleaning and preprocessing**: Not started  

- **Feature selection**: Not started  

- **Clustering implementation**: Not started  

- **Evaluation and visualization**: Not started  

## **Data Sources**

The analysis will utilize publicly available data, including but not limited to:

- **Quality and Outcomes Framework (QOF)** – Performance measures of GP practices.

- **Demographic Data** – Population characteristics, socioeconomic factors, and regional variations.

- **Additional Datasets** – Any relevant healthcare indicators.

## **Methodology**

1. **Data Preparation**

   - Collect and preprocess **QOF and demographic data**.

   - Handle missing values, outliers, and standardize features.

   

2. **Feature Engineering**

   - Select relevant performance and demographic metrics.

   - Apply **dimensionality reduction (if needed)** to improve clustering performance.

3. **Clustering Techniques**

   - Implement **K-Means clustering** to segment GP practices.

   - Use **Hierarchical clustering** for alternative segmentation and comparison.

4. **Model Evaluation**

   - Use **Elbow Method** and **Silhouette Score** to determine the optimal number of clusters.

   - Compare clustering results and interpret key patterns.

5. **Visualization & Insights**

   - Generate **heatmaps**, **scatter plots**, and **geospatial maps** to illustrate cluster characteristics.

   - Summarize findings to inform policy recommendations.

This repository is dual licensed under the [Open Government v3]([https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/) & MIT. All code can outputs are subject to Crown Copyright.