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C-reactive protein (CRP) is a protein made by the liver in response to inflammation anywhere in the body. Early and accurate detection of CRP levels is essential for diagnosing various diseases. The proposed method uses a spectroscopy to analyze urine samples and machine learning to classify them as infected or non-infected based on CRP levels. Three machine learning models were employed: Extra Trees, Random Forest, XGBoost, K-Nearest Neighbors and Decision Tree. These models aimed to classify urine samples into two categories: infected (CRP level above 10−4 μg/mL) and non-infected (CRP level below or equal 10−4 μg/mL). The accuracy of the best model, Extra Trees is up to 68%. This method has the potential for faster and more user-friendly CRP detection compared to traditional methods.
K. Cierpiak andM. Szczerska
"Optic sensor empowered by machine learning: a promising integration for C-reactive protein sensing in biological samples", Proc. SPIE 12999, Optical Sensing and Detection VIII, 129992A (20 June 2024); https://doi.org/10.1117/12.3016946
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K. Cierpiak, M. Szczerska, "Optic sensor empowered by machine learning: a promising integration for C-reactive protein sensing in biological samples," Proc. SPIE 12999, Optical Sensing and Detection VIII, 129992A (20 June 2024); https://doi.org/10.1117/12.3016946