| Title: | OS26-7 Geographic Analysis of Risk Factors for Chronic Respiratory Non-Communicable Diseases using Machine Learning |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 751-755 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS26-7 |
| Author(s): | Ayu Susilowati, Andi Prademon Yunus |
| Publication Date: | February 13, 2025 |
| Keywords: | Non-communicable diseases, Chronic respiratory diseases, Decision tree, Geo-mapping |
| Abstract: | Chronic respiratory diseases (CRDs) are a significant category of non-communicable diseases (NCDs), affecting 235 million asthma patients and 64 million COPD patients globally. In Central Java, CRDs accounted for 6% of total deaths in 2023, with WHO projecting COPD as the third leading cause of death by 2030. This study employs a decision tree machine learning approach to analyze lifestyle behaviors and environmental factors, aiming to identify key CRD risk factors and map their geographic distribution. Results indicate public transportation contributes most significantly (0.3410), followed by smoking habits and NO₂ concentration. The model achieved an RMSE of 0.40 and R² of 0.83, reflecting high predictive accuracy. This approach provides insights and enhances healthcare access in high-risk areas. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS26/OS26-7.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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