Predicting Health Insurance Costs with Neural Networks: A Complete Hyperparameter Tuning Practical Project

This article introduces a neural network regression project based on TensorFlow/Keras, aiming to accurately predict individual health insurance costs, with the core being a systematic study of 8 hyperparameter tuning techniques. The project uses the Kaggle public insurance dataset (including features such as age, gender, BMI, smoking status, etc.), which is a typical regression problem, and aims to demonstrate best practices for hyperparameter tuning.

Project Background and Significance

In the insurance industry, accurately predicting medical costs is crucial for risk pricing and policy design. Traditional actuarial science relies on statistical models, while neural networks excel at handling complex nonlinear relationships. This project provides an end-to-end solution and explores hyperparameter tuning practices.

Dataset Features

Using the Kaggle insurance dataset, the features include age (medical needs increase with age), BMI (too high or too low is associated with health issues), smoking status (a strong predictor), region (differences in medical costs), number of children (impact of family structure), and gender (smaller impact). The target variable is annual medical costs.

Neural Network Architecture Design

A multi-layer feedforward network is built using TensorFlow/Keras: the input layer receives encoded feature vectors, the hidden layers use ReLU activation to introduce nonlinearity, and the output layer directly outputs the cost value. The architecture balances expressive power and training stability.

Eight Hyperparameter Tuning Techniques

1. Batch Size: Compare gradient stability and generalization performance of large and small batches;
2. Dropout: Randomly discard neurons to prevent overfitting and explore optimal ratios;
3. Batch Normalization: Standardize layer inputs to accelerate convergence, and collaborate with Dropout to improve performance;
4. Early Stopping: Monitor validation set performance to prevent overfitting and save resources;
5. L1/L2 Regularization: L1 produces sparse weights (feature selection), L2 constrains weight size;
6. Learning Rate Scheduling: Try fixed, step decay, and adaptive strategies;
7. Optimizer Selection: Compare the performance of SGD, Adam, RMSprop, etc.

Experimental Design

The dataset is divided into training set (for model training), validation set (for hyperparameter selection), and test set (for final evaluation) to avoid data leakage.

Evaluation Metrics

Mean Squared Error (MSE) and Mean Absolute Error (MAE) are used to measure prediction bias, supplemented by scatter plots for visualization.

Result Analysis

The tuned neural network significantly outperforms the baseline model. For example, combinations like moderate Dropout ratio + batch normalization, Adam optimizer + learning rate decay, etc., perform excellently, providing references for similar regression tasks.

Practical Insights

1. Hyperparameter tuning is a core part of model development;
2. Systematic experiments (such as grid or random search) are more efficient than random attempts;
3. Combining regularization techniques (Dropout, batch normalization, etc.) can build robust models.

Future Directions

• Introduce advanced architectures like residual connections and attention mechanisms;
• Try automated tuning methods like Bayesian optimization and neural architecture search;
• Deploy as an API to implement real-time prediction.

This project provides complete code and systematic thinking practice for deep learning beginners, and the methodology can be extended to regression tasks such as house price prediction and sales prediction.