Prediction of asthma outpatients using cumulative particulate matter and machine learning algorithms: a case study in Adiyaman, Turkey
Abstract Following the 2023 earthquake in Adıyaman, Turkey, particulate matter (PM10) levels saw a significant rise, prompting the need to develop a model linking these levels to the number of asthma cases in the city. Using PM10 data from the Adıyaman urban region, we built machine learning models...
Saved in:
Main Authors: | Abdurrahman Özbeyaz, Mustafa Yıldırım, Fatih Tufaner |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2024-12-01
|
Series: | Discover Applied Sciences |
Subjects: | |
Online Access: | https://doi.org/10.1007/s42452-024-06407-x |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Impact of seasonal biometeorological conditions and particulate matter on asthma and COPD hospital admissions
by: Anna Romaszko-Wojtowicz, et al.
Published: (2025-01-01) -
The interaction between genetic predicted gut microbiome abundance and particulate matter on the risk of incident asthma in adults
by: Hehua Zhang, et al.
Published: (2025-02-01) -
Concentration of Particulate Matter and Air Humidity in Pediatric Intensive Care Unit
by: Pasaribu Hotber Edwin Rolan, et al.
Published: (2025-01-01) -
Application of deep learning techniques for analysis and prediction of particulate matter at Kota city, India
by: Lovish Sharma, et al.
Published: (2024-12-01) -
Seasonal Variability and Trends of PM10 and PM2.5 Particulate Matter Pollution in Warsaw: A Multi-Year Analysis
by: Starzomska Aleksandra, et al.
Published: (2024-12-01)