ArticleHealthcare informatics research2016
Interpretation of Clinical Data Based on C4.5 Algorithm for the Diagnosis of Coronary Heart Disease.
Article in Healthcare informatics research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Diagnosis of heart diseases: A fuzzy-logic-based approach.PloS one · 2024Article
- Combining data discretization and missing value imputation for incomplete medical datasets.PloS one · 2023Article
- Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine.Briefings in bioinformatics · 2022Review
- Research on Application of Data Mining Algorithm in Cardiac Medical Diagnosis System.BioMed research international · 2022Article
- A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea.BMC medical informatics and decision making · 2020Article
- Article
- Automatic Glaucoma Detection Method Applying a Statistical Approach to Fundus Images.Healthcare informatics research · 2018Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe interpretation of clinical data for the diagnosis of coronary heart disease can be done using algorithms in data mining. Most clinical data interpretation systems for diagnosis developed using data mining algorithms with a black-box approach cannot recognize examination attribute relationships with the incidence of coronary heart disease.
methodsThis study proposes a system to interpretation clinical examination results for the diagnosis of coronary heart disease based the decision tree algorithm. This system comprises several stages. First, oversampling is carried out by a combination of the synthetic minority oversampling technique (SMOTE), feature selection, and the C4.5 classification algorithm. System testing is done using k-fold cross-validation. The performance parameters are sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV) and the area under the curve (AUC).
resultsThe results showed that the performance of the system has a sensitivity of 74.7%, a specificity of 93.7%, a PPV of 74.2%, an NPV of 93.7%, and an AUC of 84.2%.
conclusionsThis study demonstrated that, by using C4.5 algorithms, data can be interpreted in the form of a decision tree, to aid the understanding of the clinician. In addition, the proposed system can provide better performance by category.
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