Inevitably, researchers in the field of medicine must deal with the issue of missing data. Imputation is frequently employed as a solution to this issue. Unfortunately, the perfect would overfit the experiential data distribution due to the uncertainty introduced by imputation, which would have a negative effect on the replica's generalisation presentation. It is unclear how machine learning (ML) approaches are applied in medical research despite claims that they can work around lacking data. We hope to learn if and how machine learning prediction model research discuss how they deal with missing data. Information contained in EHRs is evaluated to ensure it is accurate and comprehensive. The missing information is imputed from the recognised EHR record. The Predictive Modelling approach is used for this, and the Naive Bayesian (NB) model is then used to assess the results in terms of performance metrics related to imputation. An adaptive optimisation technique, called the Adaptive Dolphin Atom Search Optimisation (Adaptive DASO) procedure, is used to teach the NB. The created Adaptive DASO method syndicates the DASO procedure with the adaptive idea. Dolphin Echolocation (DE) and Atom Search Optimisation (ASO) come together to form DASO. This indicator of performance metrics verifies imputation's fullness.
Keywords
Missing Data, Electronic Health Records, Naïve Bayesian, Adaptive Dolphin Atom Search Optimization, Machine Learning.
T. Emmanuel, T. Maupong, D. Mpoeleng, T. Semong, B. Mphago, and O. Tabona, “A survey on missing data in machine learning,” Journal of Big Data, vol. 8, no. 1, Oct. 2021, doi: 10.1186/s40537-021-00516-9.
Dubey and A. Rasool, “Efficient technique of microarray missing data imputation using clustering and weighted nearest neighbour,” Scientific Reports, vol. 11, no. 1, Dec. 2021, doi: 10.1038/s41598-021-03438-x.
N. U. Okafor and D. T. Delaney, “Missing Data Imputation on IoT Sensor Networks: Implications for on-Site Sensor Calibration,” IEEE Sensors Journal, vol. 21, no. 20, pp. 22833–22845, Oct. 2021, doi: 10.1109/jsen.2021.3105442.
M. Pazhoohesh, A. Allahham, R. Das, and S. Walker, “Investigating the impact of missing data imputation techniques on battery energy management system,” IET Smart Grid, vol. 4, no. 2, pp. 162–175, Feb. 2021, doi: 10.1049/stg2.12011.
C.-Y. Guo, Y.-C. Yang, and Y.-H. Chen, “The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model,” Frontiers in Public Health, vol. 9, Jul. 2021, doi: 10.3389/fpubh.2021.680054.
F. B. Hamzah, F. Mohd Hamzah, S. F. Mohd Razali, and H. Samad, “A Comparison of Multiple Imputation Methods for Recovering Missing Data in Hydrological Studies,” Civil Engineering Journal, vol. 7, no. 9, pp. 1608–1619, Sep. 2021, doi: 10.28991/cej-2021-03091747.
K. Naveen Durai, R. Subha, and A. Haldorai, “Hybrid Invasive Weed Improved Grasshopper Optimization Algorithm for Cloud Load Balancing,” Intelligent Automation & Soft Computing, vol. 34, no. 1, pp. 467–483, 2022, doi: 10.32604/iasc.2022.026020.
G. H. Lee, J. Han, and J. K. Choi, “MPdist-based missing data imputation for supporting big data analyses in IoT-based applications,” Future Generation Computer Systems, vol. 125, pp. 421–432, Dec. 2021, doi: 10.1016/j.future.2021.06.042.
M. Pazhoohesh, M. S. Javadi, M. Gheisari, S. Aziz, and R. Villa, “Dealing with Missing Data in the Smart Buildings using Innovative Imputation Techniques,” IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, Oct. 2021, doi: 10.1109/iecon48115.2021.9612650.
D. Cenitta, R. V. Arjunan, and P. K V, “Missing Data Imputation using Machine Learning Algorithm for Supervised Learning,” 2021 International Conference on Computer Communication and Informatics (ICCCI), Jan. 2021, doi: 10.1109/iccci50826.2021.9402558.
Z. Alruhaymi and C. J. Kim, “Study on the Missing Data Mechanisms and Imputation Methods,” Open Journal of Statistics, vol. 11, no. 04, pp. 477–492, 2021, doi: 10.4236/ojs.2021.114030.
S. R and A. H, “Improved EPOA clustering protocol for lifetime longevity in wireless sensor network,” Sensors International, vol. 3, p. 100199, 2022, doi: 10.1016/j.sintl.2022.100199.
T. Thomas and E. Rajabi, “A systematic review of machine learning-based missing value imputation techniques,” Data Technologies and Applications, vol. 55, no. 4, pp. 558–585, Apr. 2021, doi: 10.1108/dta-12-2020-0298.
L. Hu, X. Cheng, C. Wen, and Y. Ren, “Medical prediction from missing data with max-minus negative regularized dropout,” Frontiers in Neuroscience, vol. 17, Jul. 2023, doi: 10.3389/fnins.2023.1221970.
E. Getzen, L. Ungar, D. Mowery, X. Jiang, and Q. Long, “Mining for equitable health: Assessing the impact of missing data in electronic health records,” Journal of Biomedical Informatics, vol. 139, p. 104269, Mar. 2023, doi: 10.1016/j.jbi.2022.104269.
S. Batra, R. Khurana, M. Z. Khan, W. Boulila, A. Koubaa, and P. Srivastava, “A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records,” Entropy, vol. 24, no. 4, p. 533, Apr. 2022, doi: 10.3390/e24040533.
S. Awawdeh, H. Faris, and H. Hiary, “EvoImputer: An evolutionary approach for Missing Data Imputation and feature selection in the context of supervised learning,” Knowledge-Based Systems, vol. 236, p. 107734, Jan. 2022, doi: 10.1016/j.knosys.2021.107734.
G. Nagarajan and L. D. Dhinesh Babu, “Missing data imputation on biomedical data using deeply learned clustering and L2 regularized regression based on symmetric uncertainty,” Artificial Intelligence in Medicine, vol. 123, p. 102214, Jan. 2022, doi: 10.1016/j.artmed.2021.102214.
R. Subha and A. Haldorai, “An Efficient Identification of Security Threats in Requirement Engineering Methodology,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–14, Aug. 2022, doi: 10.1155/2022/1872079.
P. I. Punitha and J. G. R. Sathiaseelan, “A Novel Two Tier Missing at Random Type Missing Data Imputation using Enhanced Linear Interpolation Technique on Internet of Medical Things,” Indian Journal Of Science And Technology, vol. 16, no. 16, pp. 1192–1204, Apr. 2023, doi: 10.17485/ijst/v16i16.60.
G. M. Borkar and A. R. Mahajan, “A secure and trust based on-demand multipath routing scheme for self-organized mobile ad-hoc networks,” Wireless Networks, vol. 23, no. 8, pp. 2455–2472, May 2016, doi: 10.1007/s11276-016-1287-y.
W. Zhao, L. Wang, and Z. Zhang, “A novel atom search optimization for dispersion coefficient estimation in groundwater,” Future Generation Computer Systems, vol. 91, pp. 601–610, Feb. 2019, doi: 10.1016/j.future.2018.05.037.
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Anbumani K
Anbumani K
Department of EIE, Sri Sairam Engineering College, Chennai, India.
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Anbumani K, Murali Dhar M S, Subramanian P, Jasmine J, Mahaveerakannan R and John Justin Thangaraj S, “Analysis of Missing Health Care Data by Effective Adaptive DASO Based Naive Bayesian Model”, Journal of Machine and Computing, vol.3, no.4, pp. 582-590, October 2023. doi: 10.53759/7669/jmc202303049.
