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

Authors

DOI:

https://doi.org/10.20535/ibb.2021.5.1.225794

Keywords:

random forest, diagnostics, speckle-tracking echocardiography, echostresstest with dobutamine, coronary heart disease

Abstract

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.

References

Voigt JU, Pedrizzetti G, Lysyansky P, Marwick TH, Houle H, Baumann R, et al. Definitions for a common standard for 2D speckle tracking echocardiography: consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging. Eur Heart J Cardiovasc Imaging. 2015;16(1):1-11. DOI: 10.1093/ehjci/jeu184

Lazoryshynets VV, Kovalenko VM, Rudenko AV, Ivaniv YA, Beshlyaga VM, Potashev SV, et al. Definition for a com-mon standard for 2D speckle-tracking echocardiography (the Association of cardiovascular surgeons of Ukraine and the Ukrainian society of cardiology working group draft consensus). Cardiol Card Surg Contin Prof Dev. 2019;2:105-29.

DOI: 10.30702/ccs.201905.02.2dst105129

Contreras-Ojeda SL, Sierra-Pardo C, Dominguez-Jimenez JA, Lopez-Bueno J, Contreras-Ortiz SH. Texture analysis of ultrasound images for pneumonia detection in pediatric patients. In: Proceedings of 22nd Symposium on Image, Signal Processing and Artificial Vision; 2019; Institute of Electrical and Electronics Engineers Inc. DOI: 10.1109/STSIVA.2019.8730238

Molinari F, Caresio C, Acharya UR, Mookiah MRK, Minetto MA. Advances in quantitative muscle ultrasonography using texture analysis of ultrasound images. Ultrasound Med Biol. 2015 Sep 1;41(9):2520-32. DOI: 10.1016/j.ultrasmedbio.2015.04.021

Kayal EB, Kandasamy D, Khare K, Bakhshi S, Sharma R, Mehndiratta A. Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging. NMR Biomed. 2021 Feb 1;34(2):17. DOI: 10.1002/nbm.4426

Boulkenafet Z, Komulainen J, Hadid A. Face spoofing detection using colour texture analysis. IEEE Trans Inf Forensics Secur. 2016;11(8):1818-30. DOI: 10.1109/tifs.2016.2555286

Kavitha P, Prabakaran S. Designing a feature vector for statistical texture analysis of brain tumor. Int J Eng Adv Technol. 2019;8(5):1228-30.

Nastenko Ie, Dykan I, Tarasiuk B, Pavlov V, Nosovets O, Babenko V, Kruglyi V, Soloduschenko V. GMDH classification of liver states in diffuse diseases based on statistical features of the ultrasound images texture. Induktivne Modelliuvannia Skladnyh System. 2020;11:54-66.

Zayed N, Elnemr HA. Statistical analysis of haralick texture features to discriminate lung abnormalities. Int J Biomed Imaging. 2015;2015:267807. DOI: 10.1155/2015/267807

Vrbik I, Van Nest SJ, Meksiarun P, Loeppky J, Brolo A, Lum JJ, et al. Haralick texture feature analysis for quantifying radiation response heterogeneity in murine models observed using Raman spectroscopic mapping. PLoS One. 2019;14(2):e0212225. DOI: 10.1371/journal.pone.0212225

Rini Novitasari DC, Lubab A, Sawiji A, Asyhar AH. Application of feature extraction for breast cancer using one order statistic, glcm, glrlm, and gldm. Adv Sci Technol Eng Syst. 2019;4(4):115-20. DOI: 10.25046/aj040413

Ali J, Khan R, Ahmad N, Maqsood I. Random forests and decision trees. Int J Comp Sci Iss. 2012;9(5):272-8.

Sharma H, Kumar S. A Survey on decision tree algorithms of classification in data mining. Int J Sci Res. 2016 Apr 5;5(4):2094-7. DOI: 10.21275/v5i4.nov162954

Fawagreh K, Gaber MM, Elyan E. Random forests: From early developments to recent advancements. Syst Sci Control Eng. 2014;2(1):602-9. DOI: 10.1080/21642583.2014.956265

Zhong J, Liu P, Li S, Huang X, Zhang Q, Huang J, et al. A comparison of three-dimensional speckle tracking echocardiography parameters in predicting left ventricular remodeling. J Healthc Eng. 2020 Aug 1;2020:1-9. DOI: 10.1155/2020/8847144

Narula S, Shameer K, Salem OAM, Dudley JT, Sengupta PP. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol. 2016 Nov;68(21):2287-95. DOI: 10.1016/j.jacc.2016.08.062

Published

2021-04-06

How to Cite

1.
Nastenko I, Maksymenko V, Potashev S, Pavlov V, Babenko V, Rysin S, Matviichuk O, Lazoryshinets V. Random Forest Algorithm Construction for the Diagnosis of Coronary Heart Disease Based on Echocardiography Video Data Streams. Innov Biosyst Bioeng [Internet]. 2021Apr.6 [cited 2024Mar.28];5(1):61-9. Available from: http://ibb.kpi.ua/article/view/225794

Issue

Section

Articles