Abstract
This study investigates oxidative stress in children exposed to cell phone and cell tower radiation by developing a predictive model for Superoxide Dismutase (SOD) activity by analyzing key parameter radiation exposure levels and fibrinogen concentration to predict SOD activity. A neural network model with polynomial feature transformation was implemented to capture non-linear relationships between exposure and oxidative stress markers. The model achieved an R^2 of 0.9096, with mean absolute error (MAE) of 0.7863, mean squared error (MSE) of 0.8273, and root mean squared error (RMSE) of 0.9955.
| Original language | English |
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| Number of pages | 5 |
| Publication status | Published - 26 Feb 2025 |
| Event | Asian Conference on Science, Technology & Medicine 2025 - Duration: 25 Feb 2025 → 26 Mar 2025 https://acstm.org/ |
Conference
| Conference | Asian Conference on Science, Technology & Medicine 2025 |
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| Abbreviated title | ACSTM 2025 |
| Period | 25/02/25 → 26/03/25 |
| Internet address |
Keywords
- neural network model
- Superoxide Dismutase (SOD)
- fibrinogen concentration
- oxidative stress