Healthcare Cost Prediction Using Neural Networks
Nathanael Mbale
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
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 callbacksStep 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 → 1Smoker:
no → 0, yes → 1Region:
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 labelstrain_labels: target values for trainingtest_labels: target values for evaluationStep 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) / stdCritical 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 callbackData 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.
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Posted Dec 27, 2025
Built a neural network to predict healthcare costs with 20% better accuracy than the target.
