Heart physiological and pathological age estimation based on wrapper deviation regression

doi:10.3969/j.issn.0253-2778.2018.09.011

Abstract

Abstract: Researches show that a person age is highly related to his heart. Heart age is very important for examining and monitoring of the heart’s state. Two algorithms for estimating the physiological and pathological age of the heart were proposed based on data mining technique. The first algorithm is based on a regression model for healthy people by using the mean absolute error (MAE), while the latter is based on a regression model for all types of people by considering the age deviation. The optimal age deviation is searched within the range of deviation candidates and is obtained by maximizing the classification accuracy. Based on the optimal age deviation and real age, the heart pathological age is obtained. The public heart dataset is used for verification of the proposed algorithm. Experimental results show that two estimated heart ages are better than the real age, with the apparent significance level the lower than 0.01. Compared with the current heart age estimation algorithm, the heart pathological age estimation algorithm can lead to the better classification capability and is more helpful with improving the classification accuracy of heart disease as a marker or feature. Besides, a new concept——heart pathological age is proposed for the first time, and which may help provide an effective marker for monitoring and supervising heart health.

Key words: heart disease, diagnosis, heart physiological age, heart pathological age, machine learning

CLC Number:

TP391

LI Yongming, XIAO Jie, WANG Pin, YAN Fang. Heart physiological and pathological age estimation based on wrapper deviation regression[J]. Journal of University of Science and Technology of China, 2018, 48(9): 762-769.

References

［1］
BENJAMIN E J, VIRANI S S, CALLAWAY C W, et al. Correction to: Heart disease and stroke statistics-2018 update: A report from the American Heart Association[J]. Circulation, 2017, 135(10):1-2.
[2] DAVIS A. Relationship of age and geographic location to heart disease health beliefs of African American women[D]. Gradworks, Walden University, 2014:1-24.
[3] FINEGOLD J A, ASARIA P, FRANCIS D P. Mortality from ischemic heart disease by country, region, and age: Statistics from World Health Organisation and United Nations[J]. International Journal of Cardiology, 2013, 168(2): 934-945.
[4] KOOPMAN C, BOTS M L, VAN D I, et al. Shifts in the age distribution and from acute to chronic coronary heart disease hospitalizations[J]. European Journal of Preventive Cardiology, 2016, 23(2):170-177.
[5] PADUR A A, HAMDAN A B, ABDULLAH T T B I P, et al. Evaluation of cardiovascular disease in patients with systemic arterial hypertension in relation to age and sex: A retrospective study in a south Indian population [J]. Jornal Vascular Brasileiro, 2017, 16(1): 11-15.
[6] CHANG J, LI B, LI J, et al. The Effects of Age, Period, and Cohort on Mortality from Ischemic Heart Disease in China [J]. International Journal of Environmental Research & Public Health, 2017, 14(1): 1-12.
[7] WU J R, MOSER D K, DEWALT D A, et al. Health literacy mediates the relationship between age and health outcomes in patients with heart failure [J]. Circulation Heart Failure, 2016, 9(1):1-19.
[8] FRANKEL S,ELWOOD P, SWEETNAM P, et al. Birthweight, body-mass index in middle age, and incident coronary heart disease[J]. Lancet, 1996, 348(9040): 1478-1480.
[9] MENDES M. Comment on “Trends in age-specific coronary heart disease mortality in the European Union over three decades: 1980-2009” [J]. Revista Portuguesa De Cardiologia, 2013, 34(39): 3017-3027.
[10] LOU Z, ALNAJAR F, ALVAREZ J M, et al. Expression-invariant age estimation using structured learning [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2018, 40(2): 365-375.
[11] GUPTA I, KAUR S, SAHNI P, et al. Novel human age estimation system based on DCT features and locality-ordinal information[C]// International Conference on Inventive Computation Technologies. Coimbatore, India: IEEE Press, 2017: 1-4.
[12] PAVLOVIC＇ S, PEREIRA C P, RUI F V D S S. Age estimation in Portuguese population: The application of the London atlas of tooth development and eruption [J]. Forensic Science International, 2017, 272: 97-103.
[13] TORRES M T, VALSTAR M F, HENRY C, et al. Small sample deep learning for newborn gestational age estimation[C]// IEEE International Conference on Automatic Face & Gesture Recognition. Washington, USA: IEEE Press, 2017: 79-86.
[14] HERRERA M J, RETAMAL R. Reliability of age estimation from iliac auricular surface in a subactual Chilean sample [J]. Forensic Science International, 2017, 275: 317-320.
[15] GMEZMARTN B, ESCAMILLAMARTNEZ E, FERNNDEZSEGUN L M, et al. Age estimation based on a radiographic study of the growing foot[J]. Journal of the American Podiatric Medical Association, 2017, 107(2):106-111.
[16] 林岚, 靳聪, 付振荣, 徐小亭.吴水才健康老年人脑年龄预测:基于尺度子配置模型的大脑连接组分析[J]. 北京工业大学学报, 2015, (6): 955-960.
[17] FRANKE K, HAGEMANN G, SCHLEUNER E, et al. Changes of individual BrainAGE, during the course of the menstrual cycle [J]. Neuroimage, 2015, 115: 1-6.
[18] 赵欣, 张雄, 王伟伟, 刘亚男, 等. 年龄相关的动态功能连接网络特征研究[J].生物医学工程学, 2017 (2): 161-167.
[19] JI X Y, CHEN Y, YE G H, et al. Detection of RAGE expression and its application to diabetic wound age estimation[J]. International Journal of Legal Medicine, 2017, 131(3): 691-698.
[20] FRANKE K, GASER C, MANOR B, et al. Advanced BrainAGE in older adults with type 2 diabetes mellitus [J]. Frontiers in Aging Neuroscience, 2013, 5(1): 90(1-9).
[21] HIRANO S, SHINOTOH H, SHIMADA H, et al. Age correlates with cortical acetylcholinesterase decline in Alzheimer's disease patients: A PET study [J]. Alzheimers & Dementia, 2012, 8(4): 531-532.
[22] LWE L C, GASER C, FRANKE K. The effect of the APOE genotype on individual BrainAGE in normal aging, mild cognitive impairment, and Alzheimer’s disease[J]. Plos One, 2016, 11(7): 1-25.
[23] DIAS P E, BEAINI T L, MELANI R F. Age estimation from dental cementum incremental lines and periodontal disease[J]. The Journal of forensic Odonto-Stomatology, 2010, 28(1): 13-21.
[24] MOYSE E, BASTIN C, SALMON E, et al. Impairment of age estimation from faces in Alzheimer's disease[J]. Journal of Alzheimers Disease, 2015, 45(2): 631-638.
[25] LI Y, LI F, WANG P, et al. Estimating the brain pathological age of Alzheimer's disease patients from MR image data based on the separability distance criterion [J]. Physics in Medicine & Biology, 2016, 61(19): 7162-7186.
[26] LI Y, LIU Y, WANG P, et al. Dependency criterion based brain pathological age estimation of Alzheimer's disease patients with MR scans [J]. Biomedical Engineering Online, 2017, 16(1): 50-69.
[27] HAN J S, SANG W L, BIEN Z. Feature subset selection using separability index matrix [J]. Information Sciences, 2013, 223(2): 102-118.
[28] ZHANG Q, HU X, ZHANG B. Comparison of l1-norm SVR and sparse coding algorithms for linear regression[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(8):1828-1833.

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