scholarly journals Heart Diseases Diagnose via Mobile Application

2021 ◽  
Vol 11 (5) ◽  
pp. 2430
Author(s):  
Mesut Güven ◽  
Fırat Hardalaç ◽  
Kanat Özışık ◽  
Funda Tuna
Keyword(s):  
Artificial Intelligence ◽  
Mobile Application ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Heart Sounds ◽  
Diagnostic Ability ◽  
Heart Abnormalities ◽  
Heart Disease Diagnosis ◽  
Clinical Experiments ◽  
Better Than

One of the oldest and most common methods of diagnosing heart abnormalities is auscultation. Even for experienced medical doctors, it is not an easy task to detect abnormal patterns in the heart sounds. Most digital stethoscopes are now capable of recording and transferring heart sounds. Moreover, it is proven that auscultation records can be classified as healthy or unhealthy via artificial intelligence techniques. In this work, an artificial intelligence-powered mobile application that works in a connectionless fashion is presented. According to the clinical experiments, the mobile application can detect heart abnormalities with approximately 92% accuracy, which is comparable to if not better than humans since only a small number of well-trained cardiologists can analyze auscultation records better than artificial intelligence. Using the diagnostic ability of artificial intelligence in a mobile application would change the classical way of auscultation for heart disease diagnosis.

scholarly journals Heart Diseases Diagnose via Artificial Intelligence-Powered Mobile Application

2021 ◽  
Author(s):  
Mesut Guven ◽  
Firat Hardalac ◽  
Kanat Ozisik ◽  
Funda Tuna
Keyword(s):  
Artificial Intelligence ◽  
Mobile Application ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Heart Sounds ◽  
Diagnostic Ability ◽  
Heart Abnormalities ◽  
Heart Disease Diagnosis ◽  
Clinical Experiments ◽  
Better Than

One of the oldest and common methods of diagnosing heart abnormalities is auscultation. Even for experienced medical doctors, it is not an easy task to detect abnormal patterns in the heart sounds. Most of the digital stethoscopes are now capable of recording and transferring the heart sounds. Moreover, it is proven that auscultation records can be classified as healthy or unhealthy via artificial intelligence techniques. In this work, an artificial intelligence-powered mobile application that works in a connectionless fashion is presented. According to the clinical experiments, the mobile application can detect heart abnormalities with approximately 92% accuracy which is comparable if not better than humans since only a small number of well-trained cardiologists can analyze auscultation records better than artificial intelligence. Using the diagnostic ability of artificial intelligence in a mobile application would change the classical way of auscultation for heart disease diagnosis.

scholarly journals Deep learning for intelligent diagnosis in thyroid scintigraphy

2021 ◽  
Vol 49 (1) ◽  
pp. 030006052098284
Author(s):  
Tingting Qiao ◽  
Simin Liu ◽  
Zhijun Cui ◽  
Xiaqing Yu ◽  
Haidong Cai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Kappa Coefficient ◽  
Diagnostic Assessment ◽  
First Year ◽  
Thyroid Scintigraphy ◽  
Diagnostic Ability ◽  
The Third ◽  
Classification Time ◽  
Better Than

Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

HEART DISEASES DETECTION FROM NOISY RECORDINGS OF SMARTPHONE DEVICES

2018 ◽  
Vol 18 (04) ◽  
pp. 1850039
Author(s):  
WEN-HSIEN HO ◽  
YENMING J. CHEN ◽  
YUZHEN ZHANG ◽  
YANYUN TAO ◽  
HSIN-WEN KUO
Keyword(s):  
Time Series ◽  
Cardiovascular Health ◽  
Heart Diseases ◽  
Vital Signs ◽  
Disease Diagnosis ◽  
Heart Murmurs ◽  
Additional Equipment ◽  
Novel Method ◽  
Testing Accuracy ◽  
Heart Disease Diagnosis

This paper aims to develop an algorithm to detect heart diseases through ordinary smartphones without additional equipment for cost accessibility. Among various vital signs emitted by organs, sounds can be easily observed and carry ample information. However, these sounds are small and noisy. Detecting anomalies involves great challenges in signal processing. This study presents a novel method that overcomes noises to estimate cardiovascular health. We use time-scale techniques in time series analysis to extract disease traits and suppress excessive ambient noises. Using datasets from PhysioNet, our model achieved a nearly 100% accuracy in heart disease diagnosis. Our approach also performs well under excessive noises for diseases producing heart murmurs. With heavy noise contaminated signals, training accuracy still closed to 100%, and the testing accuracy still remained around 84%.

scholarly journals Impact of Data Transformation: An ECG Heartbeat Classification Approach

2020 ◽  
Vol 2 ◽  
Author(s):  
Yongbo Liang ◽  
Ahmed Hussain ◽  
Derek Abbott ◽  
Carlo Menon ◽  
Rabab Ward ◽  
...  
Keyword(s):  
Heart Diseases ◽  
Data Transformation ◽  
Disease Diagnosis ◽  
Classification Performance ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Heartbeat Classification ◽  
Physiological Signal ◽  
Heart Disease Diagnosis ◽  
Transformation Methods

Cardiovascular diseases continue to be a significant global health threat. The electrocardiogram (ECG) signal is a physiological signal that plays a major role in preventing severe and even fatal heart diseases. The purpose of this research is to explore a simple mathematical feature transformation that could be applied to ECG signal segments in order to improve the detection accuracy of heartbeats, which could facilitate automated heart disease diagnosis. Six different mathematical transformation methods were examined and analyzed using 10s-length ECG segments, which showed that a reciprocal transformation results in consistently better classification performance for normal vs. atrial fibrillation beats and normal vs. atrial premature beats, when compared to untransformed features. The second best data transformation in terms of heartbeat detection accuracy was the cubic transformation. Results showed that applying the logarithmic transformation, which is considered the go-to data transformation, was not optimal among the six data transformations. Using the optimal data transformation, the reciprocal, can lead to a 35.6% accuracy improvement. According to the overall comparison tested by different feature engineering methods, classifiers, and different dataset sizes, performance improvement also reached 4.7%. Therefore, adding a simple data transformation step, such as the reciprocal or cubic, to the extracted features can improve current automated heartbeat classification in a timely manner.

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.

scholarly journals DESIGN AND IMPLEMENTATION OF AN ARTIFICIAL INTELLIGENCE-BASED HEART DISEASE DIAGNOSIS SYSTEM

2021 ◽  
Vol 3 (1) ◽  
pp. 033-040
Author(s):  
I Putu Agus Eka Pratama
Keyword(s):  
Artificial Intelligence ◽  
Heart Disease ◽  
Design Science ◽  
Diagnostic System ◽  
Science Research ◽  
Disease Diagnosis ◽  
Case Based Reasoning ◽  
Diagnosis System ◽  
Design And Implementation ◽  
Heart Disease Diagnosis

As one of the deadliest diseases in the world, heart disease requires serious treatment. The weaknesses of providing services for heart disease in Bali Province are that there is no online diagnostic system to make it easier for people to check their health conditions to find out whether they have heart disease. Based on this research, the design and implementation of a web-based online heart disease diagnosis system are carried out. The diagnostic system uses Artificial Intelligence and inputs data from the user based on several questions posed by the system. This research uses Case-Based Reasoning (CBR) algorithm with Design Science Research Methodology (DSRM) and a case study qualitative research method. The test results show that the system designed and implemented can run well and perform accurate diagnostics according to the design and user needs.

Proposed Methodology for Knowledge Acquisition: A Study on Congenital Heart Disease Diagnosis

1990 ◽  
Vol 29 (01) ◽  
pp. 30-40 ◽  
Author(s):  
F. B. Leãot ◽  
F. A. Rocha
Keyword(s):  
Knowledge Acquisition ◽  
Congenital Heart ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Congenital Heart Diseases ◽  
Entropy Analysis ◽  
Heuristic Rules ◽  
Human Power ◽  
Heart Disease Diagnosis ◽  
Multiple Experts

Abstract “Knowledge and human power are synonymous, since the ignorance of the cause frustrates the effect:…“ Francis Bacon1 This paper proposes a methodology for knowledge acquisition (KA) from multiple experts, in an attempt to elicit the heuristic rules followed by the physician in diagnosing twelve frequently occurring congenital heart diseases (CHD). Twenty-two pediatric cardiologists and twenty-three general cardiologists were interviewed with this technique; 274 interviews were conducted, 169 with the 22 experts, 105 with the 23 non-experts. A graph formalism was employed to represent their reasoning model, leading to the construction of a “mean reasoning model” for each diagnosis, separately for experts and non-experts. The results indicate that experts, compared to non-experts, tend to build knowledge representation models (KRM) that are smaller and less complex. Qualitative differences in information utilization between the two groups were also observed. Entropy analysis suggests a greater objectivity and cohesion of the experts’ model.

scholarly journals Heart Sound Classification Based on Fractal Dimension and MFCC Features Using Hidden Markov Model

2022 ◽  
Author(s):  
Mahbubeh Bahreini ◽  
Ramin Barati ◽  
Abbas Kamaly
Keyword(s):  
Fractal Dimension ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Heart Sounds ◽  
Mel Frequency Cepstral Coefficients ◽  
Sound Classification ◽  
Cepstral Coefficients

Abstract Early diagnosis is crucial in the treatment of heart diseases. Researchers have applied a variety of techniques for cardiovascular disease diagnosis, including the detection of heart sounds. It is an efficient and affordable diagnosis technique. Body organs, including the heart, generate several sounds. These sounds are different in different individuals. A number of methodologies have been recently proposed to detect and diagnose normal/abnormal sounds generated by the heart. The present study proposes a technique on the basis of the Mel-frequency cepstral coefficients, fractal dimension, and hidden Markov model. It uses the fractal dimension to identify sounds S1 and S2. Then, the Mel-frequency cepstral coefficients and the first- and second-order difference Mel-frequency cepstral coefficients are employed to extract the features of the signals. The adaptive Hemming window length is a major advantage of the methodology. The S1-S2 interval determines the adaptive length. Heart sounds are divided into normal and abnormal through the improved hidden Markov model and Baum-Welch and Viterbi algorithms. The proposed framework is evaluated using a number of datasets under various scenarios.

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