Policy Recommendations for the Strategic Implementation of Public Health Policy to Mitigate PM 2.5 Pollution through Artificial Intelligence: A Case Study on AI-Driven Lung Cancer Diagnosis

Techatach Khlaisokk; Janita Boriboon Boriboon; Jutamas  Moolkamsri; Pongphon  Chamat; Rangsiman  Songklod; Thanakon  Sangkhaloke; Saowalak  Klamsakun

Authors

Techatach Khlaisokk -
Janita Boriboon Boriboon Department of Social Sciences, Faculty of Liberal Arts and Management Science, Kasetsart University, Campus Sakon Nakhon
Jutamas Moolkamsri Department of Social Sciences, Faculty of Liberal Arts and Management Science, Kasetsart University, Campus Sakon Nakhon
Pongphon Chamat Department of Social Sciences, Faculty of Liberal Arts and Management Science, Kasetsart University, Campus Sakon Nakhon
Rangsiman Songklod Department of Social Sciences, Faculty of Liberal Arts and Management Science, Kasetsart University, Campus Sakon Nakhon
Thanakon Sangkhaloke Department of Social Sciences, Faculty of Liberal Arts and Management Science, Kasetsart University, Campus Sakon Nakhon
Saowalak Klamsakun Department of Social Sciences, Faculty of Liberal Arts and Management Science, Kasetsart University, Campus Sakon Nakhon

Keywords:

Public Health Policy Development, AI-Driven Governance, Big Data-Based Policymaking

Abstract

This study investigates the application of big data and artificial intelligence (AI) to support the formulation of evidence-based public health policy recommendations, with an emphasis on mitigating lung cancer risks linked to PM2.5 air pollution. Using 15,000 social media images, an AI model was trained via a convolutional neural network using Google’s Teachable Machine. The model achieved high performance with an accuracy of 100 percent and test accuracy of 99.5 percent, and low prediction error with loss of 0.01 percent and test loss of 1.67 percent. Key factors influencing policy implementation include policy resources, organizational capacity, and teamwork. The resulting AI model was deployed as a web application using the Python Flask framework, enabling real-time lung cancer diagnosis and rapid treatment responses. The study’s contributions include the design of a policy framework for the National Health Environment Data Center (NHEDC), the development of an AI-driven platform for real-time risk prediction, and the integration of proactive public health surveillance policy in high-risk PM2.5 areas.

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