Most literature assumptions have been drawn from public databases e.g. NHANES (National Health and Nutrition Examination Survey). Nonetheless, the sets of data are typically featured by high-dimensional timeliness, heterogeneity, characteristics and irregularity, hence amounting to valuation of these databases not being applied completely. Data Mining (DM) technologies have been the frontiers domains in biomedical studies, as it shows smart routine in assessing patients’ risks and aiding in the process of biomedical research and decision-making in developing disease-forecasting frameworks. In that case, DM has novel merits in biomedical Big Data (BD) studies, mostly in large-scale biomedical datasets. In this paper, a description of DM techniques alongside their fundamental practical applications will be provided. The objectives of this study are to help biomedical researchers to attain intuitive and clear appreciative of the applications of data-mining technologies on biomedical BD to enhance to creation of biomedical results, which are relevant in a biomedical setting.
Keywords
Big Data (BD), Data Mining (DM), Knowledge Discovery in Databases (KDD), Principal Component Analysis (PCA).
S. Zhelev and A. Rozeva, “Data analytics and machine learning with Java,” 2018.
B. Percha, “Modern clinical text mining: A guide and review,” Annu. Rev. Biomed. Data Sci., vol. 4, no. 1, pp. 165–187, 2021.
Y. A. Nastenko, “The use of cluster analysis for partitioning mixtures of multidimensional functional characteristics of complex biomedical systems,” J. Autom. Inf. Sci., vol. 28, no. 5–6, pp. 77–83, 1996.
A. Kumari and C. B. Vishwakarma, “Conventional and evolutionary order reduction techniques for complex systems,” Int. j. inf. technol. web eng., vol. 16, no. 4, pp. 74–98, 2021.
J. Große-Bley and G. Kostka, “Big data dreams and reality in Shenzhen: An investigation of smart city implementation in China,” Big Data Soc., vol. 8, no. 2, p. 205395172110451, 2021.
K. Roberts et al., “Information retrieval for biomedical datasets: the 2016 bioCADDIE dataset retrieval challenge,” Database (Oxford), vol. 2017, 2017.
D. Liu, T. Li, and D. Liang, “Incorporating logistic regression to decision-theoretic rough sets for classifications,” Int. J. Approx. Reason., vol. 55, no. 1, pp. 197–210, 2014.
B. Aslam, M. A. Azam, Y. Amin, J. Loo, and H. Tenhunen, “A high capacity tunable retransmission type frequency coded chipless radio frequency identification system,” Int. J. RF Microw. Comput-Aid. Eng., vol. 29, no. 9, p. e21855, 2019.
N. Soltanieh, Y. Norouzi, Y. Yang, and N. C. Karmakar, “A review of radio frequency fingerprinting techniques,” IEEE j. radio freq. identif., vol. 4, no. 3, pp. 222–233, 2020.
S. Goyal, “Effective software defect prediction using support vector machines (SVMs),” Int. j. syst. assur. eng. manag., 2021.
A. R. Khan, S. Khan, M. Harouni, R. Abbasi, S. Iqbal, and Z. Mehmood, “Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification,” Microsc. Res. Tech., vol. 84, no. 7, pp. 1389–1399, 2021.
S. Bakken, P. C. Dykes, S. C. Rossetti, and J. G. Ozbolt, “Patient-Centered Care Systems,” in Biomedical Informatics, Cham: Springer International Publishing, 2021, pp. 575–612.
L. J. Schlapbach, K. Reinhart, N. Kissoon, and Pediatric Sepsis Data CoLaboratory (Sepsis CoLab) and the Global Sepsis Alliance (GSA), “A pediatric perspective on World Sepsis Day in 2021: leveraging lessons from the pandemic to reduce the global pediatric sepsis burden?,” Am. J. Physiol. Lung Cell. Mol. Physiol., vol. 321, no. 3, pp. L608–L613, 2021.
J. Lyu, X. Liu, J.-F. Bi, Y. Jiao, X.-Y. Wu, and W. Ruan, “Characterization of Chinese white-flesh peach cultivars based on principle component and cluster analysis,” J. Food Sci. Technol., vol. 54, no. 12, pp. 3818–3826, 2017.
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Nico Martins
Nico Martins
Faculty of Health Science, Public university in Pretoria, Pretoria, 0002, South Africa.
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Cite this article
Trudie Steyn and Nico Martins, “The Scope, Methods and Applications of Biomedical Data Mining”, Journal of Biomedical and Sustainable Healthcare Applications, vol.2, no.1, pp. 018-025, January 2022. doi: 10.53759/0088/JBSHA202202003.
