Kronik Böbrek Hastalığının Erken Tanısı için Yeni Bir Klinik Karar Destek Sistemi
| Autor: | Can Eyüpoğlu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Health information technology
Computer science business.industry Feature extraction Disease Machine learning computer.software_genre medicine.disease Clinical decision support system Test (assessment) Random forest Clinical decision making medicine Artificial intelligence business computer Kidney disease |
| Zdroj: | European Journal of Science and Technology. |
| ISSN: | 2148-2683 |
| DOI: | 10.31590/ejosat.743652 |
| Popis: | Chronic kidney disease is a worldwide health problem. It is possible to slow or stop the progression of this disease thanks to early diagnosis and treatment. Clinical decision support systems are health information technology systems designed to assist medical doctors in clinical decision making tasks. In this study, a new clinical decision support system is proposed for the early diagnosis of chronic kidney disease. Principal component analysis (PCA) and random forests (RF) techniques are used in the feature extraction and classification phases of the proposed system, respectively. The performance of the proposed system has been compared with classical machine learning algorithms and previous studies in the literature using six different performance metrics. The test results show that the proposed system is successful and can assist doctors in making decisions for early diagnosis of chronic kidney disease. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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