Rhopalocera Optimization Algorithm Based Attribute Selection With Improved Fuzzy Artificial Neural Network (ROA - IFANN) Classifier for Coronary Artery Heart Disease Prediction in Diabetes Patients

2019 ◽  
Vol 7 (3) ◽  
pp. 926-935
Author(s):  
B. Narasimhan ◽  
A. Malathi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Coronary Artery ◽  
Optimization Algorithm ◽  
Attribute Selection ◽  
Disease Prediction ◽  
Artificial Neural ◽  
Coronary Artery Heart Disease ◽  
Diabetes Patients

An intelligent heart disease prediction system based on swarm-artificial neural network

2021 ◽  
Author(s):  
Sudarshan Nandy ◽  
Mainak Adhikari ◽  
Venki Balasubramanian ◽  
Varun G. Menon ◽  
Xingwang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Disease Prediction ◽  
Prediction System ◽  
Artificial Neural

scholarly journals Heart Disease Prediction Using Artificial Neural Network

IJARCCE ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 85-89
Author(s):  
Rachana Deshmukh ◽  
Payal Gourkhede ◽  
Sonali Rangari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Disease Prediction ◽  
Artificial Neural

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

Artificial Neural Network for Cardiovascular Disease Prediction

2021 ◽  
Vol 9 (8) ◽  
pp. 2189-2193
Author(s):  
Abhay Patil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Disease Classification ◽  
Disease Prediction ◽  
Intelligence Gathering ◽  
Binary Model ◽  
Cardio Vascular Disease ◽  
Artificial Neural ◽  
Cardio Vascular ◽  
The Ideal

Abstract: The assurance of coronary ailment a large part of the time depends upon an eccentric mix of clinical and masochist data. Considering this multifaceted nature, there exists a ton of income among clinical specialists and experts with respect to the useful and careful assumption for coronary sickness. In this paper, we cultivate a coronary disease prediction system that can help clinical specialists in expecting coronary ailment status reliant upon the clinical data of patients. Man-made intelligence-gathering strategies are amazingly useful in the clinical field by giving accurate results and quick finishes of ailments. Thusly, these techniques save part of the ideal opportunity for the two trained professionals and patients. The neural associations can be used as classifiers to expect the assurance of Cardiovascular Heart disorder. Keywords: Cardio Vascular disease, Classification, Artificial neural network, Categorical model and Binary model

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