Intelligence System for Diagnosis Level of Coronary Heart Disease with K-Star Algorithm.
- Author:
Wiharto WIHARTO
1
;
Hari KUSNANTO
;
Herianto HERIANTO
Author Information
- Publication Type:Original Article
- Keywords: Computational Intelligance; Diagnosis; Heart Disease; Classification; Machine Learning
- MeSH: Area Under Curve; Cause of Death; Classification; Coronary Disease*; Diagnosis*; Heart Diseases; Intelligence*; Machine Learning; Sensitivity and Specificity
- From:Healthcare Informatics Research 2016;22(1):30-38
- CountryRepublic of Korea
- Language:English
- 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.
