Neural network diagnosis of heart disease

2015 ◽  
Author(s):  
Ebenezer O. Olaniyi ◽  
Oyebade K. Oyedotun ◽  
Abdulkader Helwan ◽  
Khashman Adnan
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Network Diagnosis

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

Deep Neural Network with Hyperparameter Tuning for Detection of Heart Disease

2021 ◽  
Author(s):  
Fathania Firwan Firdaus ◽  
Hanung Adi Nugroho ◽  
Indah Soesanti
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Neural Network

scholarly journals Implementation of Back Propagation Artificial Neural Network for Heart Disease Abnormality Diagnosis

2021 ◽  
Vol 1764 (1) ◽  
pp. 012165
Author(s):  
Jaya Kuncara Rosa Susila ◽  
Muhammad Afit ◽  
Pujo Laksono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Back Propagation ◽  
Artificial Neural

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

Mo1935 Using Contrast-Enhanced Ultrasonography (CEUS) Time-Intensity Curves (TICs) as Classifiers in Neural Network Diagnosis of Focal Liver Lesions

2012 ◽  
Vol 142 (5) ◽  
pp. S-1004 ◽  
Author(s):  
Costin T. Streba ◽  
Dan Ionut Gheonea ◽  
Larisa D. Sandulescu ◽  
Liliana Streba ◽  
Tudorel Ciurea ◽  
...  
Keyword(s):  
Neural Network ◽  
Focal Liver Lesions ◽  
Liver Lesions ◽  
Contrast Enhanced ◽  
Contrast Enhanced Ultrasonography ◽  
Network Diagnosis

Identification of Heart Disease With Iridology Using Backpropagation Neural Network

2018 ◽  
Author(s):  
Leonardus Sandy Ade Putra ◽  
R. Rizal Isnanto ◽  
Aris Triwiyatno ◽  
Vincentius Abdi Gunawan
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Backpropagation Neural Network

scholarly journals Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Mohamad Haider Abu Yazid ◽  
Haikal Satria ◽  
Shukor Talib ◽  
Novi Azman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Parameter Tuning ◽  
Disease Classification ◽  
Network Parameter ◽  
Artificial Neural

scholarly journals KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES UNTUK MEMPREDIKSI PENYAKIT JANTUNG

2019 ◽  
Vol 15 (2) ◽  
pp. 189-194 ◽  
Author(s):  
Hendri Mahmud Nawawi ◽  
Jajang Jaya Purnama ◽  
Agung Baitul Hikmah
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Naive Bayes ◽  
Healthy Lifestyle ◽  
Naïve Bayes ◽  
The Body ◽  
Early Age ◽  
Regular Exercise ◽  
The Neural Network ◽  
Bayes Algorithm

Heart disease is one of the types of deadly diseases whose treatment must be dealt with as soon as possible because it can occur suddenly to the sufferer.  Factors of heart disease that are recognized based on the condition of the body of a sufferer need to be known from an early age so that the risk of possible instant attacks can be minimized or can be overcome in various ways such as a healthy lifestyle and regular exercise that can regulate heart health in the body.  By looking at the condition of a person's body based on sex, blood pressure, age, whether or not a smoker and some indicators that become a person's characteristics of heart disease are described in a study using the Neural Network and Naïve Bayes algorithm with the aim of comparing the level of accuracy to attributes influential to predict heart disease, so the results of this study can be used as a reference to predict whether a person has heart disease or not.

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