Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Hari Kusnanto
Gadjah Mada University, Indonesia
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Hybrid System of Tiered Multivariate Analysis and Artificial Neural Network for Coronary Heart Disease Diagnosis Wiharto Wiharto; Hari Kusnanto; Herianto Herianto
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 2: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (387.871 KB) | DOI: 10.11591/ijece.v7i2.pp1023-1031

Abstract

Improved system performance diagnosis of coronary heart disease becomes an important topic in research for several decades. One improvement would be done by features selection, so only the attributes that influence is used in the diagnosis system using data mining algorithms. Unfortunately, the most feature selection is done with the assumption has provided all the necessary attributes, regardless of the stage of obtaining the attribute, and cost required. This research proposes a hybrid model system for diagnosis of coronary heart disease. System diagnosis preceded the feature selection process, using tiered multivariate analysis. The analytical method used is logistic regression. The next stage, the classification by using multi-layer perceptron neural network. Based on test results, system performance proposed value for accuracy 86.3%, sensitivity 84.80%, specificity 88.20%, positive prediction value (PPV) 90.03%, negative prediction value (NPV) 81.80%, accuracy 86,30%  and area under the curve (AUC) of 92.1%. The performance of a diagnosis using a combination attributes of risk factors,symptoms and exercise ECG. The conclusion that can be drawn is that the proposed diagnosis system capable of delivering performance in the very good category, with a number of attributes that are not a lot of checks and a relatively low cost.
The Analysis of Performace Model Tiered Artificial Neural Network for Assessment of Coronary Heart Disease Wiharto Wiharto; Harianto Herianto; Hari Kusnanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 4: August 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (471.623 KB) | DOI: 10.11591/ijece.v7i4.pp2183-2191

Abstract

The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, the tiered approach. This study aims to analyze the performance of a tiered model assessment. The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The study is divided into the terms of the stages in the examination procedure. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The performance evaluation may indicate that the top level provides performance improvement and or reinforce the previous level. 
Co-Authors Herianto . Wiharto Wiharto