Health Simulation: Patient Risk Analysis Using Python & NumPy

Project Overview

This project simulates a mini health analytics system.

You create a synthetic dataset of 10,000 patients with the following health features:

• Age (18–90 years)
• BMI (Body Mass Index, average 25, standard deviation 5)
• Blood Pressure (average 120, standard deviation 15)
• Glucose Level (70–150 mg/dL)

Then, you classify patients into high-risk and low-risk groups based on simple health rules:

• High-risk if BMI > 30 and Blood Pressure > 140
• Low-risk otherwise

Finally, you perform basic statistical analysis, data normalization, and visualization of the patient risk categories.

This project demonstrates how to:

• Generate random data using NumPy
• Work with arrays and vectorized operations
• Apply Boolean logic and conditional classification
• Calculate mean, standard deviation, and normalize data
• Use Python dictionaries to store structured data
• Visualize results with matplotlib

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Key Concepts Used

Concept	How It Was Applied
NumPy random generation	Generate realistic patient data
NumPy arrays	Store and manipulate patient data efficiently
Boolean logic & masking	Classify high-risk vs low-risk patients
np.where	Convert True/False conditions into 1/0
Mean & Standard Deviation	Basic statistics and normalization
Python dictionaries	Map patient IDs to computed health scores
Matplotlib	Visualize patient risk distribution

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Step-by-Step Tasks

1. Random Data Generation Generate 10,000 patient records using NumPy:

   • Ages (integers 18–90)
   • BMI (normal distribution, mean 25, std 5)
   • Blood Pressure (normal distribution, mean 120, std 15)
   • Glucose (uniform distribution, 70–150)

2. Combine into a Patient Matrix Combine the four features into a 2D array with shape (10000, 4).

3. Risk Classification Classify each patient:

   • High-risk if BMI > 30 AND Blood Pressure > 140
   • Low-risk otherwise

4. Statistics & Normalization

   • Compute column-wise mean and standard deviation
   • Normalize each value: (value - mean) / std

5. Python Dictionary Bridge

   • Assign unique patient IDs: P-0001, P-0002, …
   • Compute a simple health score: BMI + BP + Glucose
   • Store in a dictionary: {patient_id: health_score}

6. Visualization

   • Bar chart showing number of high-risk vs low-risk patients

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Sample Output

• Total High-Risk Patients: 174
• Percentage of High-Risk Patients: 1.74%
• Mean BMI: 25.02
• Top 5 Glucose Values: [150.0, 149.8, 149.7, …]
• Normalized Data (first 3 patients):

[[ 0.34, -0.23, 0.12, -0.45],
 [-1.23, 0.56, -0.34, 0.12],
 [ 0.12, -1.01, 0.78, -0.56]]

• Bar Chart: Shows Low Risk and High Risk in blue color with differnet Height.

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How to Run the Project

1. Install Python 3.x

2. Install required libraries:

   pip install numpy matplotlib

3. Run the Python script:

   python health_simulation.py

4. View results in the console and bar chart visualization.

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What i have learned from this project

• Practiced NumPy basic to intermidate concepts — random generation, arrays, vectorization
• Learned simple health analytics — risk scoring and classification
• Understand statistical concepts — mean, standard deviation, normalization
• Bridge NumPy with Python dictionaries — real-world data handling
• Visualize data with matplotlib — beginner-friendly data visualization