Optimizing Neural Network Performance to Predict Coronary Heart Disease

2021 ◽  
Author(s):  
Azat Kabdullin ◽  
Maxat Kabdullin ◽  
Lyazat Naizabayeva
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Network Performance

scholarly journals The Analysis of Performace Model Tiered Artificial Neural Network for Assessment of Coronary Heart Disease

2017 ◽  
Vol 7 (4) ◽  
pp. 2183
Author(s):  
Wiharto Wiharto ◽  
Harianto Herianto ◽  
Hari Kusnanto
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Assessment Model ◽  
Model Assessment ◽  
Marquardt Algorithm ◽  
One Step ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>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. </p>

scholarly journals Hybrid System of Tiered Multivariate Analysis and Artificial Neural Network for Coronary Heart Disease Diagnosis

2017 ◽  
Vol 7 (2) ◽  
pp. 1023 ◽  
Author(s):  
Wiharto Wiharto ◽  
Hari Kusnanto ◽  
Herianto Herianto
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Multivariate Analysis ◽  
Heart Disease ◽  
System Performance ◽  
Selection Process ◽  
Disease Diagnosis ◽  
Features Selection ◽  
Diagnosis System

<span lang="EN-US">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</span><span> for</span><span lang="EN-US"> 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</span><span> for</span><span lang="EN-US"> accuracy 86.3%, sensitivity 84.80%, specificity 88.20%, positive prediction value (PPV) 90.03%, negative prediction value (NPV) 81.80%</span><span>, accuracy 86,30% </span><span lang="EN-US"> and area under the curve (AUC) of 92.1%. The performance of a diagnosis using a combination attributes of risk factors,</span><span lang="EN-US">symptoms and exercise ECG. The conclusion that can be drawn</span><span> is</span><span lang="EN-US"> that the proposed diagnosis system capable of delivering performance in the </span><span>very good</span><span lang="EN-US"> category, with a number of attributes that are not a lot of checks and a relatively low cost</span><span>.</span>

An Efficient Artificial Neural Network for Coronary Heart Disease Prediction

2021 ◽  
Vol 9 (12) ◽  
pp. 1474-1483
Author(s):  
Priyam Vinay Sheta
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Heart Disease ◽  
Early Stage ◽  
Large Population ◽  
Input Feature ◽  
The Neural Network ◽  
Artificial Neural

Abstract: Coronary heart disease is rapidly increasing over these days also with a significant number of deaths. A large population around the world is suffering from the disease. When surveys were carried out of the death rate and the number of people suffering from the coronary heart disease, it was understood that how important is the diagnosis of this disease at an early stage. The old way for detecting the disease was not found effective. This paper suggests a different method and technology to detect the disease and the proposed method is more effective than the old traditional methods. In this paper, an artificial neural network that predicts the coronary heart disease is used with 14 features as the input. Feature selection, data preprocessing, and removing irrelevant data was done before training the neural network. The backpropagation algorithm was used for making the neural network learn the features. The output of data was basically binary but the neural network was trained to give the output as a probability between 0 and 1. Two algorithms were proposed for this prediction named Logistic Regression and Artificial Neural Network but the later was selected because of the accuracy of 94%. The accuracy of Logistic Regression was 87%.

Analysis of the Effect of Hidden Layer Units on Coronary Heart Prediction Using the Radial Basis Functions Algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 297
Author(s):  
I Gede Bagus Semara Wijaya ◽  
Luh Gede Astuti
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Radial Basis Functions ◽  
Developed Countries ◽  
The United States ◽  
Basis Functions ◽  
The Neural Network ◽  
Radial Basis ◽  
Hidden Layer

Heart disease is a disease that occurs due to disturbances in the heart, especially when pumping blood so that it can cause death. Nearly half of deaths in the United States and other developed countries are caused by heart disease. Therefore, an early prognosis of heart disease is needed to prevent the risk of coronary heart disease. One thing that can be done is to predict coronary heart disease sufferers using the neural network method. This study conducted an analysis of the effect of hidden layer units on the neural network radial basis functions algorithm to predict coronary heart disease sufferers. This study obtained the highest accuracy at 10 hidden layers, namely 85.08%.

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