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Clinical Decision Support System for Assessment Coronary Heart Disease Based on Risk Factor


  • Department of Informatic, Sebelas Maret University, Indonesia
  • Department of Medichine, Gadjah Mada University, Indonesia
  • Department of Biomedical Engineering, Gadjah Mada University


Objectives: This study aims to propose a model of coronary heart disease assessment system based on risk factors. Methods/Statistical Analysis: To achieve these objectives, the model proposed system comprises several processes. First, the dimension reduction using principle component analysis (PCA). Second, classification using support vector machine. Third, validate using 10-fold cross validation in the process of training and testing. Training and testing using patient data from the Hospital Dr. Moewardi Solo Indonesia, which amounted to 120 with 12 attributes. Fourth, the system performance is measured using several parameters, namely sensitivity, specificity, area under the curve, positive prediction value, negative prediction value and accuracy. Findings: Tests on the proposed system, to process the dimensional reduction by PCA, which is followed by the method of orthogonal rotation with verimax, resulting nine attributes of the 12 attributes of risk factors. Attribute are obtained by using the variance of data from the PCA process of 71.1908%. Attribute risk factors rotation outcome is age, gender, occupation rate, total cholesterol, low-density lipoprotein, triglycerides, systolic and diastolic blood pressure and smooking. System performance prediction is generated for parameter sensitivity 84.20%, specificity 69.09%, accuracy 78.61%, positive prediction value 82.53%, negative prediction value 71.70% and the area under the curve 76.64%. Applications/Improvements: System model of clinical decision support system for the assessmen of coronary heart disease based on risk factors can be used by clinicians, as support in making clinical decisions. The proposed system provides the performance of the medium category.


Assessment, Coronary Heart Disease, Dimentional Reduction, Principle Component Analysis, Risk Factor, Support Vector Machine

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