Classical and Quantum Machine Learning for Population-Level Prediction of Heat-Related Physiological Events

TL;DR

This study compares classical and quantum machine learning methods for predicting heat-related health events at the population level, highlighting current performance differences and future potential.

Contribution

It introduces a unified framework integrating diverse datasets and evaluates classical versus quantum models for health prediction tasks.

Findings

Classical models outperform quantum models in accuracy, especially with imbalanced data.

Quantum models show meaningful learning capability and structure capture.

The framework enables future hybrid health modeling with evolving quantum hardware.

Abstract

Predicting heat-related physiological events at the population level is challenging due to the complex interactions among climatic, demographic, and socioeconomic factors, as well as the strong sparsity and seasonality of observational data. In this work, we propose a unified predictive framework that integrates heterogeneous environmental and public-health datasets and evaluates two learning paradigms within a common pipeline: classical machine learning and quantum machine learning. The methodology combines data harmonization, temporal aggregation, feature engineering, and dimensionality reduction to construct a weekly county-level population dataset. On this unified representation, we train both a classical regression baseline and a variational quantum model based on parameterized quantum circuits with angle embedding and data re-uploading. Experimental evaluation on datasets from the United States and Catalonia shows that classical models currently achieve higher predictive accuracy, particularly under conditions of strong class imbalance and sparse targets. Nevertheless, the quantum models demonstrate non-trivial learning capability and capture meaningful predictive structure in several scenarios. These results provide an empirical comparison between classical and quantum learning approaches for population-level physiological prediction and establish a methodological foundation for future hybrid health modeling as quantum hardware continues to evolve.