Stroke Classification Comparison with KNN through Standardization and Normalization Techniques
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| Název: | Stroke Classification Comparison with KNN through Standardization and Normalization Techniques |
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| Autoři: | Firmansyah, Muhammad Raihan, Astuti, Yani Parti |
| Přispěvatelé: | Muhammad Raihan Firmansyah, Yani Parti Astuti |
| Zdroj: | Advance Sustainable Science, Engineering and Technology; Vol 6, No 1 (2024): November-January; 02401012 ; 2715-4211 |
| Informace o vydavateli: | Universitas PGRI Semarang |
| Rok vydání: | 2024 |
| Sbírka: | Journal Universitas PGRI Semarang |
| Témata: | KNN, Z-Score Standardization, Min Max Normalization, Stroke Classification, Data Scaling |
| Popis: | This study explores the impact of z-score standardization and min-max normalization on K-Nearest Neighbors (KNN) classification for strokes. Focused on managing diverse scales in health attributes within the stroke dataset, the research aims to improve classification model accuracy and reliability. Preprocessing involves z-score standardization, min-max normalization, and no data scaling. The KNN model is trained and evaluated using various methods. Results reveal comparable performance between z-score standardization and min-max normalization, with slight variations across data split ratios. Demonstrating the importance of data scaling, both z-score and min-max achieve 95.07% accuracy. Notably, normalization averages a higher accuracy (94.25%) than standardization (94.21%), highlighting the critical role of data scaling for robust machine learning performance and informed health decisions. |
| Druh dokumentu: | article in journal/newspaper |
| Popis souboru: | application/pdf |
| Jazyk: | English |
| Relation: | http://journal.upgris.ac.id/index.php/asset/article/view/17685/pdf; http://journal.upgris.ac.id/index.php/asset/article/view/17685 |
| DOI: | 10.26877/asset.v6i1.17685 |
| Dostupnost: | http://journal.upgris.ac.id/index.php/asset/article/view/17685 https://doi.org/10.26877/asset.v6i1.17685 |
| Rights: | Copyright (c) 2024 Advance Sustainable Science, Engineering and Technology (ASSET) ; https://creativecommons.org/licenses/by-sa/4.0 |
| Přístupové číslo: | edsbas.EC53563E |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | This study explores the impact of z-score standardization and min-max normalization on K-Nearest Neighbors (KNN) classification for strokes. Focused on managing diverse scales in health attributes within the stroke dataset, the research aims to improve classification model accuracy and reliability. Preprocessing involves z-score standardization, min-max normalization, and no data scaling. The KNN model is trained and evaluated using various methods. Results reveal comparable performance between z-score standardization and min-max normalization, with slight variations across data split ratios. Demonstrating the importance of data scaling, both z-score and min-max achieve 95.07% accuracy. Notably, normalization averages a higher accuracy (94.25%) than standardization (94.21%), highlighting the critical role of data scaling for robust machine learning performance and informed health decisions. |
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| DOI: | 10.26877/asset.v6i1.17685 |