scholarly journals Heart Disease Diagnosis by Neural Networks

2021 ◽  
pp. 202-208
Author(s):  
Khyati Varshney ◽  
Mrinal Paliwal
Keyword(s):  
Neural Networks ◽  
Heart Disease ◽  
Mortality Rate ◽  
Intelligent Systems ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Daily Basis ◽  
The World ◽  
Multilayer Neural Networks ◽  
Heart Disease Diagnosis

In the present time the Mortality rate will be increased all around the world on their daily basis. So the cause for this might possibly be largely ascribe to the developing in the numbers of the patients with the cardiovascular patient’s diseases. To aggravate the cases, many physicians that have been known for the misdiagnosis of the patients announce heart related ailments. In this research paper, the intelligent systems have been designed in which they will help in the successful diagnosis of the forbearing to avoiding misdiagnosis. In the dataset of a UCI stat log of heart disease that will be using in this investigation. The dataset contains 14 attributes which are essential in the diagnosis of the heart diseases. A system is sculpted on the multilayer neural networks trained with convolutional & simulated convolutional neural networks. The identification of 89% was acquired from the testing of the networks.

Application of Artificial Neural Networks for Heart Disease Prediction

2020 ◽  
Vol 17 (9) ◽  
pp. 4190-4196
Author(s):  
Kumar Suyash ◽  
K. R. Shobha
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Intelligent Systems ◽  
Heart Diseases ◽  
Medical Science ◽  
Back Propagation ◽  
Diagnostic System ◽  
Science Field ◽  
The World ◽  
Significant Life

Heart related diseases are on a rise throughout the world. While the WHO estimates 31% of all deaths worldwide are caused by heart related diseases, some estimates even attribute 18 million deaths throughout the world due to such diseases. Although, the monumental strides in the field of machine learning, especially neural networks have enabled us to solve complex recognition problems, we still at large have been unable to utilize their power to the maximum in the data rich medical science field. These networks can in fact be used to construct intelligent systems which can help predict the presence of heart diseases in their early stages. Such intelligent systems shall result in significant life savings due to the readily available timely medical care and the following treatments. Encompassing the techniques of classification, a supervised learning approach of machine learning, in these intelligent systems can be aimed at pinpointing the accurate diagnosis. This paper thus, proposes a diagnostic system for predicting the presence of heart diseases using neural networks with back propagation.

scholarly journals Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters

2012 ◽  
Vol 59 (2) ◽  
pp. 190-194 ◽  
Author(s):  
Oleg Yu. Atkov ◽  
Svetlana G. Gorokhova ◽  
Alexandr G. Sboev ◽  
Eduard V. Generozov ◽  
Elena V. Muraseyeva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Coronary Heart Disease ◽  
Artificial Neural Networks ◽  
Heart Disease ◽  
Genetic Polymorphisms ◽  
Disease Diagnosis ◽  
Clinical Parameters ◽  
Artificial Neural ◽  
Heart Disease Diagnosis

scholarly journals An Efficient and Scalable Heart Disease Diagnosis System with Attribute Impact Based Weights and Genetic Correlation Analysis

2021 ◽  
Vol 35 (1) ◽  
pp. 47-53
Author(s):  
Srikanth Meda ◽  
Raveendra Babu Bhogapathi
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Heart Disease ◽  
Cardiovascular Diseases ◽  
Correlation Analysis ◽  
Disease Diagnosis ◽  
Fuzzy Neural Networks ◽  
Complex Data ◽  
Fuzzy Neural ◽  
Heart Disease Diagnosis

Fuzzy neural network (FNN) is playing a vital role in processing of complex data mining applications like medical diagnosis, speech recognition, text processing, image processing etc. Fuzzy neural networks simulate the human brain functionality with fuzzy logic decision making capabilities, to achieve more accuracy in feature selection process of complex data mining applications. Today cardiovascular diseases become a serious global health issue and approximately more than 31% of all global deaths are happening due to cardiovascular diseases reported by WHO. In order to prevent and control the cardiovascular diseases, an efficient and accurate heart disease diagnosis system (HDDS) has to be designed with the state of the art feature based data classifiers. In recent, some research articles introduced HDDS using popular data mining techniques like FNN, but they are suffering from accuracy in allocation of attribute weights and attribute correlation analysis, pattern recognition, forecasting. To address the problems in designing the HDDS, in this paper, Fuzzy Neural Networks has been used with empowered input layer and hidden layers to achieve the high accuracy and performance, while processing the huge set of medical data records. We designed an Attribute Impact calculation procedure to assign the accurate weight values to the attributes and we proposed a Genetic Correlation Analysis algorithm to do correlation analysis which helps in improving the performance.

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