Artificial neural network versus multiple logistic function to predict 25-year coronary heart disease mortality in the Seven Countries Study

<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>

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Forty-years (1961–2001) of all-cause and coronary heart disease mortality and its determinants: the Corfu cohort from the Seven Countries Study

International Journal of Cardiology ◽

10.1016/s0167-5273(02)00526-0 ◽

2003 ◽

Vol 90 (1) ◽

pp. 73-79 ◽

Cited By ~ 12

Author(s):

Demosthenes B. Panagiotakos ◽

Christina Chrysohoou ◽

Christos Pitsavos ◽

Alessandro Menotti ◽

Anastasios Dontas ◽

...

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Coronary Heart Disease Mortality ◽

Disease Mortality ◽

Seven Countries Study

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An Efficient Artificial Neural Network for Coronary Heart Disease Prediction

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39559 ◽

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%.

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