Abstract
Feature selection is an important function in classification and prediction technique, especially in medical data mining. It is embedded with the task of selecting a subset of relevant features that can be used in constructing a model. Optimization algorithms play a significant role in medical data mining especially in diagnosing chronic disease because it offers good efficiency in less computational cost and time. Also, the classification algorithms yield better results when the feature selection algorithm is based on an objective function. This paper aims to provide a review of optimized feature selection algorithms and an overview of the proposed improved teaching learning based optimization algorithm (ITLBO) for classification and prediction of chronic kidney disease (CKD). The proposed algorithm aims to reduce the number of features required for diagnosing the CKD with an objective function that optimizes the features and selects them based on the updated weight of the fitness function.
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Manonmani, M., Balakrishnan, S. (2020). Review of Optimization-Based Feature Selection Algorithms on Healthcare Dataset. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-15-0135-7_23
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DOI: https://doi.org/10.1007/978-981-15-0135-7_23
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