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Dimensionality Reduction of Healthcare Data through Niche Genetic Algorithm

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Madhu H.K., D. Ramesh

Madhu H.K. and D Ramesh. Dimensionality Reduction of Healthcare Data through Niche Genetic Algorithm. International Journal of Computer Applications 183(53):7-11, February 2022. BibTeX

	author = {Madhu H.K. and D. Ramesh},
	title = {Dimensionality Reduction of Healthcare Data through Niche Genetic Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2022},
	volume = {183},
	number = {53},
	month = {Feb},
	year = {2022},
	issn = {0975-8887},
	pages = {7-11},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2022921945},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Technology into medical health care has generated voluminous parameters for human physiological condition, forming data for high dimensions. Data which is raw makes any computing techniques complex and it is not structured.Structuring data can be a pre-processing model,but extracting useful parameters which contribute to reducing the computational complexities of any intelligent algorithm for classification and prediction is a big challenge in technology. Dimensionality reduction is a common strategy adopted by research to select appropriate parameters for further computations. In this research work Niche genetic algorithm is implemented on various healthcare datasets which extracts relevant parameters for classification and prediction of healthcare data with reduced computation complexity and increased accuracy. The proposed model is independent of any application, but restricts to structured data.


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Dimensionality reduction, Principal Component Analysis (PCA), UCI Health care dataset.