One of the Big four agenda is Universal health. This study focused on maternal health. The main aim of maternal health is usually to reduce maternal deaths. One way in aiding to reduce maternal deaths is to forecast maternal deaths using various statistical smoothing techniques. This would enable better future planning for example increase in health facilities. Shapiro-Wilk Normality Test confirmed that there was clear observable difference between the normal distribution and the data. The study hence focused on non-parametric regression methods which include Kernel and Cubic spline smoothing techniques which were applied on maternal health care data. The technique that best dealt with this type of data was identified and used to focus maternal deaths. Selecting an appropriate technique was important to achieve a good forecasting performance. The performance of the two smoothing technique was compared using MSE, MAE and RMSE and the best model identified. In both methods we have smoothing parameters. Selecting smoothing parameter goal is usually to base it on the data. According to the results obtained in the study, it is concluded that Cubic spline smoothing technique which has a lower MSE, MAE and RMSE is better than Kernel based smoothing technique. The statistical software that was used for the analysis was R. The study used maternal health care statistics data for Nakuru County.
| Published in | International Journal of Data Science and Analysis (Volume 5, Issue 6) |
| DOI | 10.11648/j.ijdsa.20190506.15 |
| Page(s) | 136-142 |
| 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), 2019. Published by Science Publishing Group |
Smoothing Technique, Cubic Spline, Kernel Smoothing
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APA Style
Lena Anyango Onyango, Thomas Mageto, Caroline Mugo. (2019). Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016). International Journal of Data Science and Analysis, 5(6), 136-142. https://doi.org/10.11648/j.ijdsa.20190506.15
ACS Style
Lena Anyango Onyango; Thomas Mageto; Caroline Mugo. Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016). Int. J. Data Sci. Anal. 2019, 5(6), 136-142. doi: 10.11648/j.ijdsa.20190506.15
AMA Style
Lena Anyango Onyango, Thomas Mageto, Caroline Mugo. Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016). Int J Data Sci Anal. 2019;5(6):136-142. doi: 10.11648/j.ijdsa.20190506.15
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Download@article{10.11648/j.ijdsa.20190506.15,
author = {Lena Anyango Onyango and Thomas Mageto and Caroline Mugo},
title = {Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016)},
journal = {International Journal of Data Science and Analysis},
volume = {5},
number = {6},
pages = {136-142},
doi = {10.11648/j.ijdsa.20190506.15},
url = {https://doi.org/10.11648/j.ijdsa.20190506.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20190506.15},
abstract = {One of the Big four agenda is Universal health. This study focused on maternal health. The main aim of maternal health is usually to reduce maternal deaths. One way in aiding to reduce maternal deaths is to forecast maternal deaths using various statistical smoothing techniques. This would enable better future planning for example increase in health facilities. Shapiro-Wilk Normality Test confirmed that there was clear observable difference between the normal distribution and the data. The study hence focused on non-parametric regression methods which include Kernel and Cubic spline smoothing techniques which were applied on maternal health care data. The technique that best dealt with this type of data was identified and used to focus maternal deaths. Selecting an appropriate technique was important to achieve a good forecasting performance. The performance of the two smoothing technique was compared using MSE, MAE and RMSE and the best model identified. In both methods we have smoothing parameters. Selecting smoothing parameter goal is usually to base it on the data. According to the results obtained in the study, it is concluded that Cubic spline smoothing technique which has a lower MSE, MAE and RMSE is better than Kernel based smoothing technique. The statistical software that was used for the analysis was R. The study used maternal health care statistics data for Nakuru County.},
year = {2019}
}
TY - JOUR T1 - Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016) AU - Lena Anyango Onyango AU - Thomas Mageto AU - Caroline Mugo Y1 - 2019/12/10 PY - 2019 N1 - https://doi.org/10.11648/j.ijdsa.20190506.15 DO - 10.11648/j.ijdsa.20190506.15 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 136 EP - 142 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20190506.15 AB - One of the Big four agenda is Universal health. This study focused on maternal health. The main aim of maternal health is usually to reduce maternal deaths. One way in aiding to reduce maternal deaths is to forecast maternal deaths using various statistical smoothing techniques. This would enable better future planning for example increase in health facilities. Shapiro-Wilk Normality Test confirmed that there was clear observable difference between the normal distribution and the data. The study hence focused on non-parametric regression methods which include Kernel and Cubic spline smoothing techniques which were applied on maternal health care data. The technique that best dealt with this type of data was identified and used to focus maternal deaths. Selecting an appropriate technique was important to achieve a good forecasting performance. The performance of the two smoothing technique was compared using MSE, MAE and RMSE and the best model identified. In both methods we have smoothing parameters. Selecting smoothing parameter goal is usually to base it on the data. According to the results obtained in the study, it is concluded that Cubic spline smoothing technique which has a lower MSE, MAE and RMSE is better than Kernel based smoothing technique. The statistical software that was used for the analysis was R. The study used maternal health care statistics data for Nakuru County. VL - 5 IS - 6 ER -
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