Optimizing urine protein detection accuracy using the K-nearest neighbors algorithm and advanced image segmentation techniques
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26 jul 2025
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Categoría del artículo: Research Article
Publicado en línea: 26 jul 2025
Recibido: 14 sept 2024
DOI: https://doi.org/10.2478/ijssis-2025-0039
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© 2025 Anton Yudhana et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Comparison of research results
| 1. | Albumin | Thakur (2021) | RGB, HSV, and Lab | RF algorithm to estimate albumin concentration using a smartphone | [ |
| 2. | Albumin | Thakur (2022) | RGB, HSV, and Lab | CNN algorithm for classifying Color in detecting albumin using a smartphone. | [ |
| 3. | Albumin | Kim (2022) | RGB | RGB extraction uses machine learning and iPhone 11 as a means of detecting color in urine. | [ |
| 4. | Protein | This study (2023) | RGB | Protein detection equipped with a digital color sensor type ELP camera. Image data are classified based on RGB and evaluated using the KNN algorithm |
Evaluation of the KNN model
| 3 | 96.7 | 97.0 | 96.7 | 96.2 |
| 10 | 86.7 | 75.8 | 86.7 | 80.7 |
| 20 | 76.7 | 60.9 | 76.7 | 67.3 |
Preparation of sample solutions
| 1. | 0.00 | 20 | Negative (−) |
| 2. | 1.00 | 20 | Plus-minus (+−) |
| 3. | 3.00 | 20 | Positive 1 (+) |
| 4. | 5.00 | 20 | Positive 2 (++) |
| 5. | 7.30 | 20 | Positive 3 (+++) |
| 6. | 11.60 | 20 | Positive 4 (++++) |
Training and test data
| − | 6 | 0 |
| +− | 24 | 0.15 |
| + | 10 | 0.3 |
| ++ | 22 | 1 |
| +++ | 30 | 3 |
| ++++ | 7 | 20 |