scholarly journals Random Forest Algorithm Construction for the Diagnosis of Coronary Heart Disease Based on Echocardiography Video Data Streams

2021 ◽  
Vol 5 (1) ◽  
pp. 61-69
Author(s):  
Ievgen Nastenko ◽  
Vitaliy Maksymenko ◽  
Sergiy Potashev ◽  
Volodymyr Pavlov ◽  
Vitalii Babenko ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Cardiovascular Diseases ◽  
Random Forest ◽  
Texture Analysis ◽  
Speckle Tracking ◽  
Speckle Tracking Echocardiography ◽  
Video Data ◽  
Random Forest Algorithm ◽  
Random Forest Method

Background. Recent studies show that cardiovascular diseases, including coronary heart disease, are the leading causes of death and one of the main factors of disability worldwide. The detection of cases of this type of disease over the past 30 years has increased from 271 million to 523 million and the number of deaths – from 12.1 million to 18.6 million. Cardiovascular diseases are the main cause of death among the population of Ukraine and, according to this indicator, the country remains one of the world leaders. Coronary heart disease is the leading factor in the loss of health in Ukraine and modern diagnostic methods, including machine learning algorithms, are increasingly being used for timely detection. Objective. According to the data of speckle-tracking echocardiography using the random forest method, construct classification algorithms for diagnosing violations of the kinematics of left ventricular contractions in patients with coronary heart disease at rest, and when using an echostress test with a dobutamine test. Methods. Speckle-tracking echocardiography was used to examine 40 patients with coronary heart disease and 16 in whom no cardiac pathology was found. Echocardiography was recorded in B mode in three positions: along the long axis, in 4-chamber, and 2-chamber positions. In total, 6245 frames of the video stream were used: 1871 – without cardiac abnormalities, and 4374 – in the presence of pathology during the examination. 56 patients (2509 frames of video data) were examined without the use of a dobutamine test and 38 patients (3736 frames of video data) – using an echostress test with a dobutamine test if no disturbances were found at rest. Dobutamine doses of 10, 20, and 40 mcg were administered under the supervision of an anesthesiologist. The data of texture analysis of images were used as informative features. To build an algorithm for detecting coronary heart disease the random forest algorithm was applied. Results. At the first stage of the study, the diagnostic algorithms norma–pathology for the state of rest and dobutamine doses of 10, 20, and 40 mcg were constructed. Before applying the algorithm the samples were randomly divided into training (70%) and test (30%). The classifiers were evaluated for accuracy, sensitivity, and specificity. According to the test samples, the accuracy of diagnostic conclusions varied from 97 to 99%. At the second stage of the study, to increase the versatility of the models, the classifier was built for all images, without dividing them into dobutamine doses. The accuracy for the test samples also ranged from 96.6 to 97.8%. To construct diagnostic algorithms by the random forest method the data of texture analysis of images were used. Conclusions. High-precision classification models were obtained using the random forest algorithm. The developed models can be applied to the analysis of echocardiograms obtained in B mode on equipment that is not equipped with the speckle tracking technology.

scholarly journals Comparative Analysis of Classification Algorithms in the Analysis of Medical Images From Speckle Tracking Echocardiography Video Data

2021 ◽  
Vol 5 (3) ◽  
pp. 153-166
Author(s):  
Olena Petrunina ◽  
Diana Shevaga ◽  
Vitalii Babenko ◽  
Volodymyr Pavlov ◽  
Sergiy Rysin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Heart Disease ◽  
Logistic Regression ◽  
Random Forest ◽  
Speckle Tracking ◽  
Speckle Tracking Echocardiography ◽  
Test Sample ◽  
Video Data ◽  
Prognostic Models ◽  
Classification Algorithms

Background. Machine learning allows applying various intelligent algorithms to produce diagnostic and/or prognostic models. Such models can be used to determine the functional state of the heart, which is diagnosed by speckle-tracking echocardiography. To determine the patient's heart condition in detail, a classification approach is used in machine learning. Each of the classification algorithms has a different performance when applied to certain situations. Therefore, the actual task is to determine the most efficient algorithm for solving a specific task of classifying the patient's heart condition when applying the same speckle-tracking echocardiography data set. Objective. We are aimed to evaluate the effectiveness of the application of prognostic models of logistic regression, the group method of data handling (GMDH), random forest, and adaptive boosting (AdaBoost) in the construction of algorithms to support medical decision-making on the diagnosis of coronary heart disease. Methods. Video data from speckle-tracking echocardiography of 40 patients with coronary heart disease and 16 patients without cardiac pathology were used for the study. Echocardiography was recorded in B-mode in three positions: long axis, 4-chamber, and 2-chamber. Echocardiography frames that reflect the systole and diastole of the heart (308 samples in total) were taken as objects for classification. To obtain informative features of the selected objects, the genetic GMDH approach was applied to identify the best structure of harmonic textural features. We compared the efficiency of the following classification algorithms: logistic regression method, GMDH classifier, random forest method, and AdaBoost method. Results. Four classification models were constructed for each of the three B-mode echocardiography positions. For this purpose, the data samples were divided into 3: training sample (60%), validation sample (20%), and test sample (20%). Objective evaluation of the models on the test sample showed that the best classification method was random forest (90.3% accuracy on the 4-chamber echocardiography position, 74.2% on the 2-chamber, and 77.4% on the long axis). This was also confirmed by ROC analysis, wherein in all cases, the random forest was the most effective in classifying cardiac conditions. Conclusions. The best classification algorithm for cardiac diagnostics by speckle-tracking echocardiography was determined. It turned out to be a random forest, which can be explained by the ensemble approach of begging, which is inherent in this classification method. It will be the mainstay of further research, which is planned to be performed to develop a full-fledged decision support system for cardiac diagnostics.

scholarly journals Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jingyi Zhang ◽  
Huolan Zhu ◽  
Yongkai Chen ◽  
Chenguang Yang ◽  
Huimin Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Speckle Tracking ◽  
Speckle Tracking Echocardiography ◽  
Screening Tools ◽  
Learning Approach ◽  
Classification Models ◽  
Ensemble Machine Learning ◽  
Machine Learning Approach

Abstract Background Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate. We use 64 two-dimensional speckle tracking echocardiography (2D-STE) features and seven clinical features to predict whether one has CHD. Methods We develop a machine learning approach that integrates a number of popular classification methods together by model stacking, and generalize the traditional stacking method to a two-step stacking method to improve the diagnostic performance. Results By borrowing strengths from multiple classification models through the proposed method, we improve the CHD classification accuracy from around 70–87.7% on the testing set. The sensitivity of the proposed method is 0.903 and the specificity is 0.843, with an AUC of 0.904, which is significantly higher than those of the individual classification models. Conclusion Our work lays a foundation for the deployment of speckle tracking echocardiography-based screening tools for coronary heart disease.

scholarly journals Спекл-трекинг эхокардиография в оценке систоло-диастолической функции левого желудочка при ишемической болезни сердца с учетом особенностей строения миокарда

2021 ◽  
pp. 19-38
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Left Ventricle ◽  
Speckle Tracking ◽  
Longitudinal Strain ◽  
Circumferential Strain ◽  
Speckle Tracking Echocardiography ◽  
Radial Strain

В обзоре представлены диагностические возможности спекл-трекинг эхокардиографии (speckle tracking echocardio graphy) для оценки систоло-диастолической функции левого желудочка при ишемической болезни сердца с учетом особенностей строения миокарда. Спиральное строение миокарда и взаимодействие разнонаправленных волокон левого желудочка усложняют задачу оценки регионарной и глобальной сократимости левого желудочка. Спекл-трекинг эхокардиография позволяет измерить деформацию миокарда в продольном, циркулярном и радиальном направлениях. Обсуждается клиническое использование метода при наиболее опасных формах ишемической болезни сердца: остром инфаркте миокарда и нестабильной стенокардии. Спекл-трекинг эхокардиография позволяет выявлять компенсаторное увеличение деформации интактного миокарда, а также ротации левого желудочка при нарушениях локальной сократимости. Измерение глобальных значений деформации, скручивания и раскручивания левого желудочка представляет прогностическую информацию у больных с острым инфарк том миокарда и нестабильной стенокардией. Несмотря на преимущества, существуют препятствия, затрудняющие использование данного метода в клинической практике. Основные из них - качество ультразвукового изображения и отсутствие общепринятых нормативных значений величин деформации. Ключевые слова: спекл-трекинг эхокардиография, продольная деформация, циркулярная деформация, радиальная деформация, левый желудочек, ишемическая болезнь сердца speckle tracking echocardiography, longitudinal strain, circumferential strain, radial strain, left ventricle, coronary heart disease

scholarly journals Expert System for Early Diagnosis of Heart Disease Using the Random Forest Method

2019 ◽  
Vol 3 (1) ◽  
pp. 15
Author(s):  
Yogi Prawira Putra ◽  
Duman Care Khrisne ◽  
I Made Arsa Suyadnya
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Expert System ◽  
Random Forest ◽  
Early Diagnosis ◽  
Programming Language ◽  
Confusion Matrix ◽  
Training Data ◽  
Random Forest Method ◽  
Initial Symptoms

In Indonesia, coronary heart disease continues to grow. However, the efforts to prevention it can still be done by diagnosing the initial symptoms caused by using an expert system. This study was designed to build an expert system application to diagnose early coronary disease by random forest methods. The application interface was built using the PHP programming language using framework bootstrap, and uses the python programming language to build a random forest. To make an early diagnosis of coronary heart disease, a decision tree was built by training data from the UCI Dataset Machine Learning Repository using the random forest method. Followed by patient classification data that has been collected through 13 questions to get the diagnosis. The diagnosis results were normal, stadium 1, stadium 2, stadium 3 and stadium 4. Based on the tests that had been carried out, the application was able to provide results in accordance with the sample data collected using a confusion matrix resulting in an accuracy of 92.25% +/- 0.62 with 70% precision, remember 46%, which obtained a score of f0,5 72%.

scholarly journals Echocardiography-Based Screening for Coronary Heart Disease Using an Ensemble Machine Learning Approach

2020 ◽  
Author(s):  
Jingyi Zhang ◽  
Huolan Zhu ◽  
Yongkai Chen ◽  
Chenguang Yang ◽  
Huimin Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Speckle Tracking ◽  
Speckle Tracking Echocardiography ◽  
Screening Tools ◽  
Learning Approach ◽  
Classification Models ◽  
Ensemble Machine Learning ◽  
Machine Learning Approach

Abstract Background: Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate. We use 64 two-dimensional speckle tracking echocardiography (2D-STE) features and seven clinical features to predict whether one has CHD. Methods: We develop a machine learning approach that integrates a number of popular classification methods together by model stacking, and generalize the traditional stacking method to a two-step stacking method to improve the diagnostic performance. Results: By borrowing strengths from multiple classification models though the proposed method, we improve the CHD classification accuracy from around 70% to 87.7% on the testing set. The sensitivity of the proposed method is 0.903 and the specificity is 0.843, with an AUC of 0.904, which is significantly higher than those of the individual classification models. Conclusions: Our work lays a foundation for the deployment of speckle tracking echocardiography-based screening tools for coronary heart disease.

scholarly journals Intracardiac flow visualization using high‐frame rate blood speckle tracking echocardiography: Illustrations from infants with congenital heart disease

2021 ◽  
Vol 38 (4) ◽  
pp. 707-715
Author(s):  
Massimiliano Cantinotti ◽  
Pietro Marchese ◽  
Martin Koestenberger ◽  
Raffaele Giordano ◽  
Giuseppe Santoro ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Flow Visualization ◽  
Speckle Tracking ◽  
Congenital Heart ◽  
Speckle Tracking Echocardiography ◽  
Frame Rate ◽  
High Frame Rate ◽  
Intracardiac Flow

Prediction of atherosclerosis in children and the possibility of early prevention of coronary heart disease

1999 ◽  
Vol 80 (4) ◽  
pp. 296-297
Author(s):  
O. I. Pikuza ◽  
V. N. Oslopov ◽  
H. M. Vakhitov ◽  
A. A. Babushkina ◽  
S. E. Nikolsky
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Coronary Artery ◽  
Cardiovascular Diseases ◽  
Childhood And Adolescence ◽  
Early Prevention ◽  
Cerebrovascular Pathology ◽  
Artery Disease

Cardiovascular diseases caused by atherosclerosis (coronary artery disease, cerebrovascular pathology, etc.) are responsible for 40-50% of all deaths in adults. Of particular concern to clinicians is the emerging unfavorable tendency to "rejuvenate" these diseases. Currently, the fact that atherosclerosis (AS) begins to form in childhood and adolescence is indisputable.

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