Abstract
Feature selection is required for effective and efficient data analysis. It is a preprocessing step in data mining which reduces the inputs for analytical task. It is effective in improving the results and increasing the learning accuracy by reducing the data dimensionality and selecting only the relevant variables for modeling. In this paper, we have analyzed the importance of feature selection for classification on maternal health data of Uttar Pradesh for the year 2015–16. In this study, the wrapper method with best first greedy approach is used for features subset selection. The reduced dataset has shown approximately 4.6% increase in the balanced accuracy of the generated classifier over the classifier generated on the high-dimensional original data.
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Abbreviations
- ANC:
-
Ante natal care
- ASHAs:
-
Accredited social health activists
- JSSK:
-
Janani Shishu Suraksha Karyakaram
- JSY:
-
Janani Suraksha Yojna
- MDGs:
-
Millennium development goals
- MMR:
-
Maternal mortality rate
- NPD:
-
Non-priority district
- NRHM:
-
National rural health mission
- PD:
-
Priority district
- SBA:
-
Skilled birth attendant
- UNFPA:
-
United nations population fund
- UNICEF:
-
United nations international children's emergency fund
- WHO:
-
World health organisation
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Gupta, S., Singh, S.N., Jain, P.K. (2021). Feature Selection on Public Maternal Healthcare Dataset for Classification. In: Abraham, A., Castillo, O., Virmani, D. (eds) Proceedings of 3rd International Conference on Computing Informatics and Networks. Lecture Notes in Networks and Systems, vol 167. Springer, Singapore. https://doi.org/10.1007/978-981-15-9712-1_49
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DOI: https://doi.org/10.1007/978-981-15-9712-1_49
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