Early detection of liver disease using data visualisation and classification method
| Autor: | Mingrui Shi, Haibo Shi, Zeyu Zheng, Yonglai Zhang, Xiaofeng Zhou |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
medicine.diagnostic_test
business.industry Computer science Early detection Health Informatics Pattern recognition computer.software_genre medicine.disease Field (computer science) Data description Support vector machine Liver disease Data visualization Signal Processing medicine Classification methods Data mining Artificial intelligence Liver function tests business computer |
| Zdroj: | Biomedical Signal Processing and Control. 11:27-35 |
| ISSN: | 1746-8094 |
| DOI: | 10.1016/j.bspc.2014.02.006 |
| Popis: | Detection of early-stage liver diseases is a challenge in medical field. Automated diagnostics based on machine learning therefore could be very important for liver tests of patients. This paper investigates 225 liver function test records (each record include 14 features), which is a subset from 1000 patients’ liver function test records that include the records of 25 patients with liver disease from a community hospital. We combine support vector data description (SVDD) with data visualisation techniques and the glowworm swarm optimisation (GSO) algorithm to improve diagnostic accuracy. The results show that the proposed method can achieve 96% sensitivity, 86.28% specificity, and 84.28% accuracy. The new method is thus well-suited for diagnosing early liver disease. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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