Healthc Inform Res.  2016 Jan;22(1):30-38. 10.4258/hir.2016.22.1.30.

Intelligence System for Diagnosis Level of Coronary Heart Disease with K-Star Algorithm

Affiliations
  • 1Department of Informatic, Sebelas Maret University, Surakarta, Indonesia. wi_harto@yahoo.com
  • 2Department of Biomedical Engineering, Gadjah Mada University, Yogyakarta, Indonesia.
  • 3Department of Medichine, Gadjah Mada University, Yogyakarta, Indonesia.
  • 4Department of Mechanical and Industrial Engineering, Gadjah Mada University, Yogyakarta, Indonesia.

Abstract


OBJECTIVES
Coronary heart disease is the leading cause of death worldwide, and it is important to diagnose the level of the disease. Intelligence systems for diagnosis proved can be used to support diagnosis of the disease. Unfortunately, most of the data available between the level/type of coronary heart disease is unbalanced. As a result system performance is low.
METHODS
This paper proposes an intelligence systems for the diagnosis of the level of coronary heart disease taking into account the problem of data imbalance. The first stage of this research was preprocessing, which included resampled non-stratified random sampling (R), the synthetic minority over-sampling technique (SMOTE), clean data out of range attribute (COR), and remove duplicate (RD). The second step was the sharing of data for training and testing using a k-fold cross-validation model and training multiclass classification by the K-star algorithm. The third step was performance evaluation. The proposed system was evaluated using the performance parameters of sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), area under the curve (AUC) and F-measure.
RESULTS
The results showed that the proposed system provides an average performance with sensitivity of 80.1%, specificity of 95%, PPV of 80.1%, NPV of 95%, AUC of 87.5%, and F-measure of 80.1%. Performance of the system without consideration of data imbalance provide showed sensitivity of 53.1%, specificity of 88,3%, PPV of 53.1%, NPV of 88.3%, AUC of 70.7%, and F-measure of 53.1%.
CONCLUSIONS
Based on these results it can be concluded that the proposed system is able to deliver good performance in the category of classification.

Keyword

Computational Intelligance; Diagnosis; Heart Disease; Classification; Machine Learning

MeSH Terms

Area Under Curve
Cause of Death
Classification
Coronary Disease*
Diagnosis*
Heart Diseases
Intelligence*
Machine Learning
Sensitivity and Specificity

Figure

  • Figure 1 Intelligence system for the diagnosis of the proposed.

  • Figure 2 Intelligence system performance using K-star (K*). PPV: positive prediction value, NPV: negative prediction value, AUC: area under the curve.

  • Figure 3 Intelligence system performance using R-SCOR-R-K(*). PPV: positive prediction value, NPV: negative prediction value, AUC: area under the curve.

  • Figure 4 Comparison of the system when it is not and using R-SCOR-RD. PPV: positive prediction value, NPV: negative prediction value, AUC: area under the curve.


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