PreStroke_ML: a machine learning approach to heat stroke prediction

Md. Zahurul Haque, Mimuza Tazvia and Afreen Sultana Kuna

International Journal of Complexity in Applied Science and Technology, 2026, vol. 2, issue 1, 97-107

Abstract: Heatstroke is an increasingly critical public health concern, intensified by rising global temperatures and the growing frequency of extreme heat events. This study addresses the urgent need for timely and accurate heatstroke risk prediction by leveraging machine learning techniques. The primary objective is to develop a predictive model capable of identifying individuals at risk based on environmental and physiological data. An extensive dataset of 81,215 instances and 69 features underwent thorough preprocessing and analysis. Four machine learning algorithms - decision tree, random forest, logistic regression, and light gradient boosting machine (LightGBM) - were implemented and evaluated. Among these, LightGBM achieved the highest accuracy of 99.93%, demonstrating superior predictive performance and generalisation capability, as validated through confusion matrices and training-validation accuracy curves. Feature selection played a crucial role in optimising model effectiveness. The findings underscore the potential of machine learning as a valuable tool in predictive healthcare. Future work will focus on integrating real-time sensor data, enabling personalised risk assessments, and deploying a mobile-based alert system to enhance heatstroke prevention. This research contributes to proactive public health strategies through an AI-driven framework for early detection and intervention.

Keywords: heatstroke prediction; AI in healthcare; public health; early warning system; health risk prevention. (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.inderscience.com/link.php?id=151888 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcast:v:2:y:2026:i:1:p:97-107

Access Statistics for this article

More articles in International Journal of Complexity in Applied Science and Technology from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().