|
|
|
| The application of cost-sensitive algorithm in the diagnosis of right ventricular failure by echocardiography |
| LI Jie1, LI Xiao-qing2, NIU Hui-min3, MA Lin1, SUN Yu-wei1 |
1. Department of Ultrasonography, North China University of Science and Technology Affiliated Hospital, Tangshan Hebei 063000, China;
2. Department of Ultrasound Medicine, KaiLuan General Hospital, Tangshan Hebei 063000, China;
3. Ultrasonography Department, Hebei General Hospital, Shijiazhuang 050057, China |
|
|
|
|
Abstract Objective: To improve the efficacy of echocardiographic method in evaluating right ventricular function, and to explore the value of cost-sensitive machine learning method model based on ultrasound parameters in the diagnosis of right ventricular failure. Methods: Eighty-two subjects with right heart failure(RHF group) and 106 subjects without right heart failure(non-RHF group) were enrolled in this study. Multi-echocardiographic indices were selected as features, including the right ventricular fractional area change(RVFAC), tricuspid annular plane systolic excursion(TAPSE), tricuspid flow peak velocity at early diastole(E), tricuspid annular peak systolic velocity(Sm) and peak early diastolic velocity(Em), Tei index, isovolumic acceleration(IVA) at tricuspid annular, and the diameter of the left ventricle, right ventricle, right atrium were measured and calculated. Cost-sensitive naive bayes analysis was used to build diagnosis model by using those parameters. The model was tested by 10-fold cross-validation. The diagnostic results of the cost-sensitive model and the classical naive bayes model were compared. Results: Compared with any single parameter, machine learning model can achieve better accuracy in diagnosis of right heart failure(91%). The cost-sensitive naive bayes method achieved a smaller overall cost. Conclusion: The diagnostic model established by machine learning is better than any single ultrasound parameter, which can reduce the diagnostic cost compared with conventional method. This method has potential practical value in screening of right ventricular failure by imaging.
|
|
Received: 11 March 2018
|
|
|
|
|
|
[1]Selly J, Iriart X, Roubertie F, et al. Multivariable Assessment of the Right Ventricle By Echocardiography in Patients with Repaired Tetralogy of Fallot Undergoing Pulmonary Valve Replacement: a Comparative Study with Magnetic Resonance Imaging[J]. Archf Cardiovasc Dis, 2015, 108(1): 5-15.
[2]中华医学会心血管病学分会. 右心衰竭诊断和治疗中国专家共识[J]. 中华心血管病杂志，2012，40（6）：449-461.
[3]Rudski LG, Lai WW, Afilalo J, et al. Guidelines for the Echocardiographic Assessment of the Right Heart in Adults: a Report From the American Society of Echocardiography: Endorsed By the European Association of Echocardiography, a Registered Branch of the European Society of Cardiology, and the Canadian Society of Echocardiography[J]. J Am Soc Echocardiogr, 2010, 23(7): 685-713.
[4]Lang RM, Badano LP, Mor-avi V, et al. Recommendations for Cardiac Chamber Quantification By Echocardiography in Adults: an Update From the American Society of Echocardiography and the European Association of Cardiovascular Imaging[J]. J Am Soc Echocardiogr, 2015, 28(1): 1-39.
[5]Simon MA. Assessment and Treatment of Right Ventricular Failure[J]. Nat Rev Cardiol, 2013, 10(4): 204-218.
[6]Dutta T, Aronow WS. Echocardiographic Evaluation of the Right Ventricle: Clinical Implications[J]. Clin Cardiol, 2017, 40(8): 542-548.
[7]Venkatachalam S, Wu G, Ahmad M. Echocardiographic assessment of the right ventricle in the current era: Application in clinical practice[J]. Echocardiography, 2017, 34(12): 1930-1947.
[8]Focardi M, Cameli M, Carbone S, et al. Traditional and Innovative Echocardiographic Parameters for the Analysis of Right Ventricular Performance in Comparison with Cardiac Magnetic Resonance[J]. Eur Heart J Cardiovasc Imaging, 2015, 16(1): 47-52.
[9]Yang F, Wang H, Mi H, et al. Using Random Forest for Reliable Classification and Cost-sensitive Learning for Medical Diagnosis[J]. Bmc Bioinformatics, 2009, 10(Suppl 1): S22.
[10]Dinunzio GM. A New Decision to Take for Cost-sensitive Naive Bayes Classifiers[J]. Inf Process Manag, 2014, 50(5): 653-674.
[11]Shapiro SD. Refining Lung Cancer Screening Criteria in the Era of Value-based Medicine[J]. PLoS Med, 2017, 14(2): e1002226.
[12]Gatos I, Tsantis S, Spiliopoulos S, et al. A Machine-learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography[J]. Ultrasound Med Biol, 2017, 43(9): 1797-1810.
[13]Medvedofsky D, Addetia K, Hamilton J, et al. Semi-automated Echocardiographic Quantification of Right Ventricular Size and Function[J]. Int J Cardiov Imaging, 2015, 31(6): 1149-1157. |
|
|
|
