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On the goodness of fit of parametric and non-parametric data mining techniques: the case of malaria incidence thresholds in Uganda

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Abstract

To identify which data mining technique (parametric or non-parametric) best fits the predictions on imbalanced malaria incidence dataset. The researchers compared parametric techniques in form of naïve Bayes and logistic regression against non-parametric techniques in form of support vector machines and artificial neural networks and their goodness of fit and prediction was assessed using 10-fold and 5-fold cross-validation on an independent validation dataset set to determine which model best fits the predictions on imbalanced malaria incidence dataset. The 10-fold cross-validation outperformed the 5-fold cross-validation in all performance metrics with the naïve Bayes classifier attaining accuracy of 69% with a sensitivity of 90.9%, a specificity of 55.6%, a precision of 55.6% and F-measure score of 69.0%, the logistic regression achieved an accuracy of 65.5% with a sensitivity of 83.3%, a specificity of 52.9%, a precision of 55.6% and F-measure score of 66.7%, the support vector machines achieved an accuracy of 82.8% with a sensitivity of 88.2%, a specificity of 75.0%, a precision of 83.3%, and F-measure score of 85.7% whereas the artificial neural networks registered an accuracy of 89.7% with a sensitivity of 94.1%, a specificity of 83.3%, a precision of 88.9%, and F-measure score of 91.4%. Non-parametric data mining techniques in form of artificial neural networks and support vector machines outperformed the parametric data mining technique in form of naïve Bayes in making predictions emanating from imbalanced malaria incidence dataset on account of registering higher F-measure values of 91.4% and 85.7% respectively.

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Availability of data and material

The data was sourced from the ministry of health( www.health.go.ug ), Uganda Bureau of Statistics ( www.ubos.org ) and Uganda National Meteorological Authority ( www.unma.go.ug ) and it has been availed/uploaded as supplementary material.

Code availability

The program scripts/code can be availed by the first author upon request.

Notes

  1. www.unma.go.ug

  2. www.ubos.org

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Acknowledgements

The authors extend their appreciation to Mr. Douglas Candia and Mr. Frank Namugera who contributed to improving this research. This research was partly funded by Makerere University through the Staff Development, Welfare and Retirement Benefits Committee (SDWRBC).

Funding

This study was partly funded by Makerere University through the Staff Development, Welfare and Retirement Benefits Committee (SDWRBC).

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Authors and Affiliations

  1. School of Statistics and Planning, Makerere University, Kampala, Uganda

    Francis Fuller Bbosa

  2. School of Computing and Informatics Technology, Makerere University, Kampala, Uganda

    Francis Fuller Bbosa, Josephine Nabukenya & Peter Nabende

  3. Department of Statistics, College of Science, Sultan Qaboos University, Muscat, Oman

    Ronald Wesonga

Authors
  1. Francis Fuller Bbosa
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  2. Josephine Nabukenya
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  3. Peter Nabende
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  4. Ronald Wesonga
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Contributions

FFB was involved in drafting the proposal, data collection, data preprocessing, data analysis, model designing and writing the manuscript. JN, PN and RW were supervisors of the work. All authors read amd approved the final manuscript.

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Correspondence to Francis Fuller Bbosa.

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Bbosa, F.F., Nabukenya, J., Nabende, P. et al. On the goodness of fit of parametric and non-parametric data mining techniques: the case of malaria incidence thresholds in Uganda. Health Technol. 11, 929–940 (2021). https://doi.org/10.1007/s12553-021-00551-9

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