The Analysis of Facial Areas to Identify CHD Risks Based on Facial Textures
Abstract
Early screening for coronary heart disease (CHD) remains insufficiently addressed, underscoring the need for a more effective screening tool. Previous studies have reported a classification accuracy of only 72.73%, which is inadequate. This study aimed to develop and evaluate a machine learning model or diagnose CHD using facial texture features and to compare the performance across different facial regions to provide recommendations for improvement. The research involved constructing a machine learning model that extracted texture features from six facial regions of interest (ROIs) using the gray level co-occurrence matrix (GLCM) and employed an artificial neural network (ANN) algorithm. The datasets were full-face images of CHD patients (positive) and healthy people (negative). The face parts identified were the right crow’s feet, right canthus, nose bridge, forehead, left canthus, and left crow’s feet. A total of 132 (72 positive and 60 negative CHD) datasets were divided into 80% (n = 106) training data and 20% (n = 26) testing data. The developed model achieved a notable accuracy of 76.9%. The findings revealed that two facial regions—canthus and forehead—demonstrated excellent accuracy of 80.97% and 90%, respectively. Meanwhile, the crow’s feet and nose bridge regions showed good accuracies at 73.50% and 65%, respectively. Based on the results, this research has proven to be able to become a model for early CHD screening with good accuracy and faster execution.
References
S.I. Ayon, M.M. Islam, and M.R. Hossain, “Coronary artery heart disease prediction: A comparative study of computational intelligence techniques,” IETE J. Res., vol. 68, no. 4, pp. 2488–2507, Jul./Aug. 2022, doi: 10.1080/03772063.2020.1713916.
C. Shao, J. Wang, J. Tian, and Y. Tang, “Coronary artery disease: From mechanism to clinical practice,” in Coronary Artery Disease: Therapeutics and Drug Discovery, M. Wang, Ed., Singapore, Singapore: Springer, 2020, pp. 1–36.
B. Jin, L. Cruz, and N. Gonçalves, “Deep facial diagnosis: Deep transfer learning from face recognition to facial diagnosis,” IEEE Access, vol. 8, pp. 123649–123661, Jun. 2020, doi: 10.1109/ACCESS.2020.3005687.
Y.A. Rismawan et al., “Development of coronary heart disease diagnosis system based on facial imagery,” in Proc. 3rd Conf. Fundam. Appl. Sci. Adv. Technol. 2022, 2022, p. 020003 , doi: 10.1063/5.0180182.
J. Wang et al., “A stacking-based model for non-invasive detection of coronary heart disease,” IEEE Access, vol. 8, pp. 37124–37133, Feb. 2020, doi: 10.1109/ACCESS.2020.2975377.
S. Lin et al., “Face analysis for coronary heart disease diagnosis,” in 2019 12th Int. Congr. Image Signal Process. BioMed. Eng. Inform. (CISP-BMEI), 2019, pp. 1–5, doi: 10.1109/CISP-BMEI48845.2019.8966020.
S. Lin et al., “Feasibility of using deep learning to detect coronary artery disease based on facial photo,” Eur. Heart J., vol. 41, no. 46, pp. 4400–4411, Dec. 2020, doi: 10.1093/eurheartj/ehaa640.
J. Qiang et al., “Review on facial-recognition-based applications in disease diagnosis,” Bioengineering, vol. 9, no. 7, pp. 1–16, Jul. 2022, doi: 10.3390/bioengineering9070273.
U. Thirunavukkarasu, S. Umapathy, K. Janardhanan, and R. Thirunavukkarasu, “A computer aided diagnostic method for the evaluation of type II diabetes mellitus in facial thermograms,” Phys. Eng. Sci. Med., vol. 43, no. 3, pp. 871–888, Sep. 2020, doi: 10.1007/s13246-020-00886-z.
S. Gondane, A. Maherda, and R. Kothiwala, “To study the prevalence of metabolic syndrome and dyslipidemia in patients of xanthelasma palpebrarum at a tertiary care hospital,” Asian J. Diabetol., vol. 21, no. 3, pp. 10–14, Oct. 2020.
H.-C. Chang, C.-W. Sung, and M.-H. Lin, “Serum lipids and risk of atherosclerosis in xanthelasma palpebrarum: A systematic review and meta-analysis,” J. Am. Acad. Dermatol., vol. 82, no. 3, pp. 596–605, Mar. 2020, doi: 10.1016/j.jaad.2019.08.082.
P. Kampar, Q. Anum, and S. Lestari, “The correlation between lipid profile and xanthelasma,” Berk. Ilmu Kesehat. Kulit Kelamin, vol. 32, no. 2, pp. 119–125, Aug. 2020, doi: 10.20473/bikk.V32.2.2020.119-125.
A.K.I. Suman et al., “Association of xanthelasma palpebrarum (XP) with cardiovascular disease (CVD) risk factors,” Asian J. Med. Biol. Res., vol. 5, no. 4, pp. 324–329, Dec. 2019, doi: 10.3329/ajmbr.v5i4.45271.
B.I. Fitrasanti, A. Onggo, W. Sugirman, and B. Ciptawan, “Prevalence of cutaneous markers in coronary artery disease cases,” Bali Med. J., vol. 10, no. 2, pp. 877–880, Aug. 2021, doi: 10.15562/bmj.v10i2.2531.
E.R. Dougherty, Digital Image Processing Methods. New York, NY, USA: CRC Press, 1994.
S. Ibrahim et al., “Automated platelet counter with detection using k-means clustering,” Ann. Emerg. Technol. Comput. (AETiC), vol. 7, no. 5, pp. 39–49, Oct. 2023, doi: 10.33166/AETIC.2023.05.004.
S. Sellán, J. Kesten, A.Y. Sheng, and A. Jacobson, “Opening and closing surfaces,” ACM Trans. Graph., vol. 39, no. 6, pp. 1–13, Dec. 2020, doi: 10.1145/3414685.3417778.
R.M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern., vol. SMC-3, no. 6, pp. 610–621, Nov. 1973, doi: 10.1109/TSMC.1973.4309314.
B.M. Jebin, M.A. Rejula, and G. Eberlein, “Neonatal Seizure detection using GLCM feature extraction & AlexNet classification,” Multimed. Tools Appl., Oct. 2024, doi: 10.1007/s11042-024-18779-8.
N. Iqbal, R. Mumtaz, U. Shafi, and S.M.H. Zaidi, “Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms,” PeerJ Comput. Sci., vol. 7, pp. 1–26, May 2021, doi: 10.7717/PEERJ-CS.536.
M. Madanan, Neural Network and Deep Learning. Uttar Pradesh, India: BlueRose Publishers, 2022.
S. Parodi, D. Verda, F. Bagnasco, and M. Muselli, “The clinical meaning of the area under a receiver operating characteristic curve for the evaluation of the performance of disease markers,” Epidemiol. Health, vol. 44, pp. 1–10, Oct. 2022, doi: 10.4178/epih.e2022088.
M.G. Nugraha, S. Utari, D. Saepuzaman, and F. Nugraha, “Redesign of students’ worksheet on basic physics experiment based on students’ scientific process skills analysis in Melde’s law,” in 4th Int. Semin. Math. Sci. Comput. Sci. Educ., 2018, pp. 1–8, doi: 10.1088/1742-6596/1013/1/012038.
© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.
1.png)
