title: Interpretation of Clinical Data Based on C4.5 Algorithm for the Diagnosis of Coronary Heart Disease.
abstract: OBJECTIVE
  The 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.
  
  
  METHODS
  This 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).
  
  
  RESULTS
  The 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%.
  
  
  CONCLUSIONS
  This 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.
authors.affiliation: Department of Informatic, Sebelas Maret University, Surakarta, Indonesia.; Department of Biomedical Engineering, Gadjah Mada University, Yogyakarta, Indonesia.
authors.forename: Wiharto
authors.initials: W
authors.lastname: Wiharto
authors.affiliation: Department of Biomedical Engineering, Gadjah Mada University, Yogyakarta, Indonesia.; Department of Medicine, Gadjah Mada University, Yogyakarta, Indonesia.
authors.forename: Hari
authors.initials: H
authors.lastname: Kusnanto
authors.affiliation: Department of Biomedical Engineering, Gadjah Mada University, Yogyakarta, Indonesia.; Department of Mechanical εt Industrial Engineering, Gadjah Mada University, Yogyakarta, Indonesia.
authors.forename: Herianto
authors.initials: H
authors.lastname: Herianto
keywords: Algorithms; Data Mining; Decision Tree; Diagnosis; Heart Diseases