Diagnosis of Cardiac Diseases Using Ranked Parameters for Machine Learning
Authors: 
Shashi Kant Tiwari, Shweta Sinha, Karamjit Kaur Sekhon, Ram Sewak SinghAuthors Info & Claims
Shashi Kant Tiwari, Shweta Sinha, Karamjit Kaur Sekhon, Ram Sewak SinghAuthors Info & ClaimsArticle No.: 155, Pages 1 - 6
Abstract
Abstract: This paper introduces an innovative approach to evaluate the classification performance of parameters derived from diverse spectral and nonlinear investigation methods in the analysis of cardiac heart disease using ranking techniques. The objective of this study is to identify the most suitable methods for application in a clinical setting. Ranking methods are employed to assign ranks to features extracted from heart rate variability (HRV) data by spectral and chaos investigation methods, organizing them based on their clinical significance. Initially, 30 and 10 different features are extracted from HRV time-series signals using popular HRV analysis methods, including spectral and nonlinear investigation techniques. Among these, 5 spectral features are derived from the auto-regression (AR) model, 15 from multiscale Wavelet packet (MSWP) decomposition, and 10 from higher-order spectra (HOS). The top ten features from each method are selected and ranked using Fisher score, Wilcoxon, receiver operating characteristics (ROC), entropy, and Bhattacharya space methods. Numerical results demonstrate that features at the third level of MSWP decomposition exhibit the lowest p-value (<<0.0001). Given that features with the lowest p-value possess the highest discrimination ability, the third-level MSWP features outperform other features extracted by AR and HOS spectral methods in HRV dataset analysis. Furthermore, features such as CD, DFA:α₂, ApEn, SaEn, and SD1/SD2, derived from chaos investigation methods, consistently display the lowest p-values compared to all considered features. Consequently, these features are identified as optimal choices for HRV analysis of datasets.
References
[1]
Cardiovascular diseases (CVDs) https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds Available online: (accessed on 27 June 2022)
[2]
Robinson S. Priorities for Health Promotion and Public Health. Routledge; 2021. Cardiovascular disease; pp. 355–393. [Google Scholar]
[3]
Baig M., Gazzaz Z.J., Gari M.A., Al-Attallah H.G., Al-Jedaani K.S., Mesawa A.T., Al-Hazmi A.A. Prevalence of obesity and hypertension among university students' and their knowledge and attitude towards risk factors of cardiovascular disease (CVD) in jeddah, Saudi arabia. Pakistan J. Med. Sci. 2015;31:816. [PMC free article] [ ] [Google Scholar]
[4]
Chiauzzi E., Rodarte C., DasMahapatra P. Patient-centered activity monitoring in the self-management of chronic health conditions. BMC Med. 2015;13:1–6. [PMC free article] [ ] [Google Scholar]
[5]
Mohr D.C., Zhang M., Schueller S.M. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annu. Rev. Clin. Psychol. 2017;13:23. [PMC free article] [ ] [Google Scholar]
[6]
Adans-Dester C., Bamberg S., Bertacchi F., Caulfield B., Chappie K., Demarchi D., Erb M.K., Estrada J., Fabara E., Freni M., Can MHealth technology help mitigate the effects of the COVID-19 pandemic? IEEE Open J. Eng. Med. Biol. 2020:1. 1. [PMC free article] [ ] [CrossRef] [Google Scholar]
[7]
Aroganam G., Manivannan N., Harrison D. Review on wearable technology sensors used in consumer sport applications. Sensors. 2019;19:1983. [PMC free article] [ ] [Google Scholar]
[8]
[8]Chow C.K., Ariyarathna N., Islam S.M.S., Thiagalingam A., Redfern J. MHealth in cardiovascular health care. Heart Lung Circ. 2016;25:802–807. [ ] [CrossRef] [Google Scholar]
[9]
Sousa P.S., Sabugueiro D., Felizardo V., Couto R., Pires I., Garcia N.M. In: Mobile Health. Adibi S., editor. vol. 5. Springer International Publishing; Cham: 2015. MHealth sensors and applications for personal aid; pp. 265–281. (Springer Series in Bio-/Neuroinformatics). [Google Scholar]
[10]
Meskó B., Drobni Z., Bényei É., Gergely B., Gy\Horffy Z. Digital health is a cultural transformation of traditional healthcare. mHealth. 2017;3 [PMC free article] [ ] [Google Scholar]
[11]
Pires I.M., Denysyuk H.V., Villasana M.V., Sá J., Lameski P., Chorbev I., Zdravevski E., Trajkovik V., Morgado J.F., Garcia N.M. Mobile 5P-medicine approach for cardiovascular patients. Sensors. 2021;21:6986. [PMC free article] [ ] [CrossRef] [Google Scholar]
[12]
Gardes J., Maldivi C., Boisset D., Aubourg T., Vuillerme N., Demongeot J. Maxwell®: an unsupervised learning approach for 5P medicine. Stud. Health Technol. Inf. 2019;264:1464–1465. [ ] [CrossRef] [Google Scholar]
[13]
B. Hu, S. Wei, D. Wei, L. Zhao, G. Zhu, and C. Liu, “Multiple Time Scales Analysis for Identifying Congestive Heart Failure Based on Heart Rate Variability,” IEEE Access, vol. 7, pp. 17862–17871, 2019.
[14]
S. Bashir, U. Qamar, and F. H. Khan, “IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework,” J. Biomed. Inform., vol. 59, no. 1, pp. 185–200, 2016.
[15]
R. Fadnavis, K. Dhore, D. Gupta, J. Waghmare, and D. Kosankar, “Heart disease prediction using data mining,” in Journal of Physics: Conference Series, 2021, vol. 1913, no. 1.
[16]
J. T. Ramshur, “Design, Evaluation, and Applicaion of Heart Rate Variability Analysis Software (Hrvas),” J. Chem. Inf. Model., vol. 53, no. 9, pp. 1689–1699, 2013.
[17]
A. Boardman, F. S. Schlindwein, A. P. Rocha, and A. Leite, “A study on the optimum order of autoregressive models for heart rate variability,” Physiol. Meas., vol. 23, no. 2, pp. 325–336, 2002.
[18]
R. N. Khushaba, S. Kodagoda, S. Lal, and G. Dissanayake, “Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm,” IEEE Trans. Biomed. Eng., vol. 58, no. 1, pp. 121–131, 2011.
[19]
K. C. Chua, V. Chandran, U. R. Acharya, and C. M. Lim, “Cardiac state diagnosis using higher order spectra of heart rate variability,” J. Med. Eng. Technol., vol. 32, no. 2, pp. 145–155, 2008.
[20]
U. Rajendra Acharya, K. Paul Joseph, N. Kannathal, C. M. Lim, and J. S. Suri, “Heart rate variability: a review.,” Med. Biol. Eng. Comput., vol. 44, no. 12, pp. 1031–51, 2006.
[21]
P. W. Kamen, H. Krum, and A. M. Tonkin, “Poincaré plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans.,” Clin. Sci. (Lond)., vol. 91, no. 2, pp. 201–8, 1996.
[22]
M. Zanin, L. Zunino, O. A. Rosso, and D. Papo, “Permutation entropy and its main biomedical and econophysics applications: A review,” Entropy, vol. 14, no. 8. pp. 1553–1577, 2012.
[23]
H. Azami and J. Escudero, “Improved multiscale permutation entropy for biomedical signal analysis: Interpretation and application to electroencephalogram recordings,” Biomed. Signal Process. Control, vol. 23, pp. 28–41, 2016.
[24]
D. Kugiumtzis and A. Tsimpiris, “Measures of Analysis of Time Series ( MATS ):,” J. Stat. Softw., vol. 33, no. 5, 2010.
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November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
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Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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