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Erschienen in:
23.03.2018
Health care data analysis using evolutionary algorithm
Erschienen in: The Journal of Supercomputing | Ausgabe 6/2020
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Abstract
Assessment of huge amount of data is the difficult task in the health care industry. Hence, it here brings the important need of the data mining in identifying the relationship between the data attributes. In this research work, an assessment model for the health care analysis is developed with the preprocessing steps of performing data cleaning by applying normalization with outlier detection by applying the k-means clustering. Then, the preprocessed data are subjected to the dimensionality reduction process by performing the Feature Selection task. Then, the selected features are analyzed by the wrapper model named SVM-based improved recursive feature selection, and its accuracy is evaluated and compared with the other traditional classifiers such as Naïve Bayes. The analysis demonstrates that the planned perfect has accomplished a regular correctness of 98.79% of health care dataset such as Pima Indians diabetes. It demonstrates that the planned technique has achieved improved consequences.