Publication: The Effect of Heuristic Methods Toward Performance of Health Data Analysis
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Springer Science and Business Media Deutschland GmbH
Analysis and prediction of health data make essential contributions to the detection, control, and prevention of diseases in the early stages without special examinations. In the analysis of health data, the balance of the datasets, the accuracy and completeness of the data, and the selection of features to represent the disease are very important as they affect the performance of machine learning methods. They have also become popular in various health data analysis studies such as classification of diseases, selection of features to represent the disease, imputation of missing value in dataset since heuristic methods give successful result in the optimization of many problems. In this chapter, various studies that combine heuristic methods and machine learning algorithms for health data analysis between 2010 and 2021 have been examined. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
▪ Part of the Studies in Computational Intelligence book series (SCI,volume 1039).
Feature Selection, Health Data, Heuristic Algorithms, Missing Value, Optimization Algorithms, Unbalanced Dataset
Nizam Ozogur, H., & Orman, Z. (2022). The Effect of Heuristic Methods Toward Performance of Health Data Analysis. In Next Generation Healthcare Informatics (pp. 147-171). Singapore: Springer Nature Singapore.