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

Aideyan Donald Osaro; Efuwape Biodun Tajudeen

doi:doi:10.11648/j.bsi.20220703.11

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

Aideyan Donald Osaro, Efuwape Biodun Tajudeen

Published in Biomedical Statistics and Informatics (Volume 7, Issue 3)

Received: 12 February 2022 Accepted: 4 March 2022 Published: 28 July 2022

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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

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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

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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

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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

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@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}
}

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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  -

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Author Information

Aideyan Donald Osaro

Dept of Mathematical Sciences, Taraba State University, Jalingo, Nigeria
Efuwape Biodun Tajudeen

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

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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

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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

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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

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@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}
}

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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