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Healthcare Cost Prediction Using Neural Networks

Quick Overview

Problem: Predict individual healthcare costs using regression analysis on demographic and health data, achieving Mean Absolute Error (MAE) under $3,500.

Solution: Built a deep neural network regression model with feature normalization and categorical encoding to predict medical expenses from patient characteristics.

Impact: Achieved $2,800 MAE (20% better than the requirement), demonstrating proficiency in regression modeling, data preprocessing, and TensorFlow/Keras implementation.

Completed Project

https://colab.research.google.com/drive/1TzGEH3w3DyVbxumFz2LufzQEgTDMPYgX?usp=drive_link

Step-by-Step Implementation

Step 1: Environment Setup & Data Loading

What: Imported TensorFlow 2.x, Keras, and a healthcare insurance dataset.

Dataset: insurance.csv containing patient information and medical costs.

Key Libraries:

  • TensorFlow / Keras for neural networks
  • Pandas for data manipulation
  • Matplotlib for visualization
  • tensorflow_docs for training callbacks

Step 2: Data Exploration

What: Examined dataset structure and features.

Features Identified:

  • Numerical: age, bmi, children
  • Categorical: sex, smoker, region
  • Target Variable: expenses (healthcare costs)

Initial Analysis: Used dataset.tail() to inspect data structure and identify preprocessing needs.

Step 3: Categorical Data Encoding

Problem: Neural networks require numerical inputs, but the dataset contains text categories.

Solution: Manual mapping of categorical variables to numerical values.

  • Sex: male → 0, female → 1
  • Smoker: no → 0, yes → 1
  • Region:
    • southwest → 0
    • southeast → 1
    • northwest → 2
    • northeast → 3

Key Action: Applied string stripping to handle whitespace before mapping.

Impact: Converted all features to numerical format for model compatibility.

Step 4: Train-Test Split

Split Ratio: 80% training, 20% testing.

Method: Random sampling using frac=0.2 for the test set.

Implementation:

  • Test dataset: 20% random sample from original data
  • Train dataset: Remaining 80%, created by dropping test indices

Label Separation:

  • Popped the expenses column to create labels
  • train_labels: target values for training
  • test_labels: target values for evaluation

Step 5: Feature Normalization

Problem: Features exist on different scales (age: 18–64, BMI: 15–50, expenses: $1k–$60k), causing training instability.

Solution: Z-score normalization (standardization).

  • Calculated mean and standard deviation from training data only
  • Applied formula: (x − mean) / std

Critical Decision: Used training statistics for both training and test data to prevent data leakage.

Impact:

  • Faster convergence during training
  • Improved gradient descent stability
  • Better model generalization

Step 6: Neural Network Architecture Design

Model Type: Sequential feedforward neural network for regression.

Architecture:

  • Input layer: 6 features after encoding
  • Hidden Layer 1: 64 neurons, ReLU activation
  • Hidden Layer 2: 64 neurons, ReLU activation
  • Output Layer: 1 neuron for continuous cost prediction

Rationale:

  • Two hidden layers capture non-linear relationships
  • ReLU activation avoids vanishing gradients
  • 64 neurons balance learning capacity and overfitting risk

Step 7: Model Compilation

Optimizer: RMSprop (learning rate = 0.001)

  • Adaptive learning rate suitable for regression
  • Handles sparse gradients effectively

Loss Function: Mean Squared Error (MSE)

  • Standard loss for regression
  • Penalizes large errors more heavily

Metrics Tracked:

  • MAE (Mean Absolute Error): primary evaluation metric
  • MSE (Mean Squared Error): secondary monitoring metric

Step 8: Model Training

Training Configuration:

  • Epochs: 500
  • Validation split: 20% of training data
  • Verbose: 0 with EpochDots callback

Data Used: Normalized training data

Monitoring: Validation loss tracked to detect overfitting.

Result: Model learned relationships between patient features and healthcare costs.

Step 9: Model Evaluation

Test Evaluation: model.evaluate() on normalized test data.

Key Metric: Mean Absolute Error (MAE)

Performance Achieved: $2,800 MAE

Target: ≤ $3,500

✅ Beat target by $700 (20% improvement).

Interpretation: Model predictions are, on average, $2,800 away from actual healthcare costs.

Step 10: Prediction Visualization

What: Generated a scatter plot of true versus predicted expenses.

Plot Elements:

  • X-axis: Actual healthcare costs (test_labels)
  • Y-axis: Model predictions
  • Diagonal line: Perfect prediction reference
  • Equal aspect ratio for accurate comparison

Interpretation:

  • Points close to the diagonal indicate accurate predictions
  • Scatter distribution highlights model strengths and weaknesses
  • Visual confirmation of $2,800 MAE performance

Step 11: Final Validation

Automated Test: Built-in evaluation verifying MAE ≤ $3,500.

Result:

✅ "You passed the challenge. Great job!"

Verification: Model successfully generalizes to unseen test data.