| Peer-Reviewed

Wavelet Analysis of Aberrant Observations in the Rate of Inflow of Patients in Some Diseaes in Kogi State, Nigeria

Received: 12 February 2022     Accepted: 4 March 2022     Published: 28 July 2022
Views:       Downloads:
Abstract

In recent years, the method of wavelet analysis has been opened to researchers. Wavelet analysis analyses data at different level of decomposition and can capture the characteristics of data series in all decomposition level. In this research work, data was collected on the medical records of the inflow of patients for medication on Malaria fever and Anemia from Grimard Catholic Hospital Anyigba, Kogi State, Nigeria (1993 to 2014). The data was analysed by wavelet methods to detect the aberrant observations over the period under study for the two diseases respectively using a proposed threshold. A total of ten and nine Aberrant Observations (AOs) were detected from the analysis of the original data collected on Malaria Fever and Anemia respectively. At the first and second level of decomposition (resolution), a total of seven and one AO(s) were respectively detected for both Malaria Fever analysis and Anemia analysis. The results obtained showed that the AOs detected in the analysis of the original data maintain the same or closely the same positions as that obtained from the analysis of the decomposed data for the two diseases. It was observed that the inflow of patients in the months of September, October and November into the hospital for medication on the two diseases were more. The Time plot for Malaria Fever and Anemia in the appendix respectively showed that there was no month that fewer patients reported to the hospital for medication.

Published in Biomedical Statistics and Informatics (Volume 7, Issue 3)
DOI 10.11648/j.bsi.20220703.11
Page(s) 41-48
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Wavelet, Decomposition, Resolution, Aberrant Observations, Diseases

References
[1] Barnet, V. & Lewis, T. (1994), Outliers in statistical data, John Wiley, ISBN 0-471-93094-6, Chichester.
[2] Bilen, C., Huzurbazar, S. (2002), “Wavelet-Based Detection of Outliers in Time Series”, Journal of Computational and Graphical Statistics, Volume 11, Number 2, 311-327.
[3] Caraballo H, “Emergency department management of mosquito-borne illness: malaria, dengue, and west nile virus”, 2014.
[4] Chen, Z.; Fu, A. & Tang, J., (2002). Detection of outliered Patterns, Dept. of CSE, Chinese University of Hong Kong.
[5] Combes, J. M.; Grossman A. & Tchamitchian P. (1989) Wavelets: Time-Frequency Methods and Phase Space, Second Edition, Springer-Verlag, New York.
[6] Daubechies, I. (1992). Ten Lectures on Wavelets. Philadelphia, Pa.: SIAM.
[7] Donoho, D. L., Johnstone I. M, Kerkyacharian G, and Picard D, 1993, April, Density estimation by wavelet thresholding, Preprint, Department of Statistics, Stanford University.
[8] Donoho D. L. and Johnstone I. M. (1994). “Ideal Spatial Adaptation by Wavelet Shrinkage”, Biometrika, Vol. 81, pp. 425-455.
[9] Ziou D and Tabbone S. Edge detection techniques: Anoverview. International Journal of Pattern Recognition and Image Analysis, 8 (4): 537–559, 1998.
[10] Eric Poulin and Colin Yu, 2005: Outlier Detection & Analysis.
[11] Grossmann, A. & Morlet, J. (1984) Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape, SIAM J. Math. Anal. Vol. 15, N° 4, pp. 723-736.
[12] Grubbs, F. E. (1969), Procedures for detecting outlying observations in samples, Technometrics 11, pp. 1-21.
[13] G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: A multi-resolution clustering approach for verylarge spatial databases. In Proceedings of 24th VLDB Conference, 1998.
[14] Haar, A. (1910). Zur Theorie der orthogonal unktionen systeme. Math. Ann., 69, 331–371.
[15] Han, J. & Kamber M. (2001) Data Minings Concepts and Techniques, Morgan Kauffman Publishers.
[16] Hawkins, D. (1980), Identification of Outliers, Chapman and Hall, London.
[17] Hodge, V. J. (2004), A survey of outlier detection methodologies, Kluver Academic Publishers, Netherlands, January 2004.
[18] Johnstone, I. (1994). Minimax bayes, asymptotic minimax and sparse wavelet priors. In Statistical Decision Theory and Related Topics, V (S. Gupta andJ. Berger, eds.). Springer-Verlag, New York, 303–326. MR1286310.
[19] Kantardzic, M. (2003). Data mining Concepts, Models, Methods and Algorithms. IEEE Transactions on neural networks, Vol. 14, N. 2, March 2003.
[20] Kern, M.; Preimesberger, T; Allesch, M.; Pail, R.; Bouman, J. & Koop, R. (2005). Outlier detection algorithms and their performance in GOCE gravity field processing, Journal of Geodesy, Vol. 78, pp. 509-519, January 2005.
[21] Mallat, S. (1999). A Wavelet Tour of Signal Processing. 2nd ed. Academic Press, San Diego. MR1614527.
[22] Moore, D. S. and McCabe G. P. (1999), Introduction to the Practice of Statistics., Freeman & Company.
[23] Ramasmawy R.; Rastogi R. & Kyuseok S. (2000). Efficient algorithms for mining outliers from large data sets. Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 427-438, ISBN 1-58113-217-4, Dallas, Texas, United States.
[24] Polikar, R. 2001. The wavelet Tutorial. Fundamental concepts and an overview of the wavelet theory.
[25] Rodak, Bernadette F. (2007). Hematslogy: clinical principles and applications (3rd ed.). Philadelphia. Saunders.
[26] Shekhar S, Huang Y, Wu W, Lu C, and Chawla S. What’s special about spatial data mining: three case studies. In Data Mining for Scientific and Engineering Applications. V. Kumar, R. Grossman, C. Kamath, R. Namburu (eds.), 2001.
[27] Shekhar S, Lu C, and Zhang P. Detecting graph-basedspatial outliers. International Journal of Intelligent Data Analysis (IDA), 6 (5): 451–468, 2002.
[28] Li T, Li Q, Zhu S, and Ogihara M. A survey on wavelet applications in data mining. SIGKDD Explorations, 4 (2): 49–67, 2002.
[29] Wang, Y. (1995) Jump and sharp cusp detection by wavelets. Biometrika, Vol. 82, pp. 385-397.
[30] Weiss, L. G. (1994). Wavelets and Wideband Correlation Processing, Signal Processing magazine IEEE, Vol. 11, pp. 13-32, ISSN 1053-5888, January 1994.
[31] WHO. March 2014: Malaria Fact sheet W “94”.
[32] World Health Organisation (2010). Guidelines for the treatment of malaria (2nd ed.). Geneva: World Health Organisation P. ix. ISBN 9789241547925.
[33] Meyer Y. Wavelet and Operators. Cambridge University Press, 1992.
[34] Yu, D.; Sheikholeslami G. & Zhang, A. (2002) Find Out: Finding Outliers in Very Large Datasets. Knowledge and Informations Systems, vol. 4, pp. 387-412, Springer-Verlag, London.
Cite This Article
  • APA Style

    Aideyan Donald Osaro, Efuwape Biodun Tajudeen. (2022). Wavelet Analysis of Aberrant Observations in the Rate of Inflow of Patients in Some Diseaes in Kogi State, Nigeria. Biomedical Statistics and Informatics, 7(3), 41-48. https://doi.org/10.11648/j.bsi.20220703.11

    Copy | Download

    ACS Style

    Aideyan Donald Osaro; Efuwape Biodun Tajudeen. Wavelet Analysis of Aberrant Observations in the Rate of Inflow of Patients in Some Diseaes in Kogi State, Nigeria. Biomed. Stat. Inform. 2022, 7(3), 41-48. doi: 10.11648/j.bsi.20220703.11

    Copy | Download

    AMA Style

    Aideyan Donald Osaro, Efuwape Biodun Tajudeen. Wavelet Analysis of Aberrant Observations in the Rate of Inflow of Patients in Some Diseaes in Kogi State, Nigeria. Biomed Stat Inform. 2022;7(3):41-48. doi: 10.11648/j.bsi.20220703.11

    Copy | Download

  • @article{10.11648/j.bsi.20220703.11,
      author = {Aideyan Donald Osaro and Efuwape Biodun Tajudeen},
      title = {Wavelet Analysis of Aberrant Observations in the Rate of Inflow of Patients in Some Diseaes in Kogi State, Nigeria},
      journal = {Biomedical Statistics and Informatics},
      volume = {7},
      number = {3},
      pages = {41-48},
      doi = {10.11648/j.bsi.20220703.11},
      url = {https://doi.org/10.11648/j.bsi.20220703.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20220703.11},
      abstract = {In recent years, the method of wavelet analysis has been opened to researchers. Wavelet analysis analyses data at different level of decomposition and can capture the characteristics of data series in all decomposition level. In this research work, data was collected on the medical records of the inflow of patients for medication on Malaria fever and Anemia from Grimard Catholic Hospital Anyigba, Kogi State, Nigeria (1993 to 2014). The data was analysed by wavelet methods to detect the aberrant observations over the period under study for the two diseases respectively using a proposed threshold. A total of ten and nine Aberrant Observations (AOs) were detected from the analysis of the original data collected on Malaria Fever and Anemia respectively. At the first and second level of decomposition (resolution), a total of seven and one AO(s) were respectively detected for both Malaria Fever analysis and Anemia analysis. The results obtained showed that the AOs detected in the analysis of the original data maintain the same or closely the same positions as that obtained from the analysis of the decomposed data for the two diseases. It was observed that the inflow of patients in the months of September, October and November into the hospital for medication on the two diseases were more. The Time plot for Malaria Fever and Anemia in the appendix respectively showed that there was no month that fewer patients reported to the hospital for medication.},
     year = {2022}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Wavelet Analysis of Aberrant Observations in the Rate of Inflow of Patients in Some Diseaes in Kogi State, Nigeria
    AU  - Aideyan Donald Osaro
    AU  - Efuwape Biodun Tajudeen
    Y1  - 2022/07/28
    PY  - 2022
    N1  - https://doi.org/10.11648/j.bsi.20220703.11
    DO  - 10.11648/j.bsi.20220703.11
    T2  - Biomedical Statistics and Informatics
    JF  - Biomedical Statistics and Informatics
    JO  - Biomedical Statistics and Informatics
    SP  - 41
    EP  - 48
    PB  - Science Publishing Group
    SN  - 2578-8728
    UR  - https://doi.org/10.11648/j.bsi.20220703.11
    AB  - In recent years, the method of wavelet analysis has been opened to researchers. Wavelet analysis analyses data at different level of decomposition and can capture the characteristics of data series in all decomposition level. In this research work, data was collected on the medical records of the inflow of patients for medication on Malaria fever and Anemia from Grimard Catholic Hospital Anyigba, Kogi State, Nigeria (1993 to 2014). The data was analysed by wavelet methods to detect the aberrant observations over the period under study for the two diseases respectively using a proposed threshold. A total of ten and nine Aberrant Observations (AOs) were detected from the analysis of the original data collected on Malaria Fever and Anemia respectively. At the first and second level of decomposition (resolution), a total of seven and one AO(s) were respectively detected for both Malaria Fever analysis and Anemia analysis. The results obtained showed that the AOs detected in the analysis of the original data maintain the same or closely the same positions as that obtained from the analysis of the decomposed data for the two diseases. It was observed that the inflow of patients in the months of September, October and November into the hospital for medication on the two diseases were more. The Time plot for Malaria Fever and Anemia in the appendix respectively showed that there was no month that fewer patients reported to the hospital for medication.
    VL  - 7
    IS  - 3
    ER  - 

    Copy | Download

Author Information
  • Dept of Mathematical Sciences, Taraba State University, Jalingo, Nigeria

  • Dept of Mathematical Sciences, Olabisi Onabanjo University, Ago-Iwoye, Nigeria

  • Sections