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Design of an intelligent system for diabetes prediction by integrating rough set theory and genetic algorithm

Design of an intelligent system for diabetes prediction by integrating rough set theory and genetic algorithm

Sengupta, Shampa, Pal, Kumud Ranjan and Garg, Vivek ORCID: 0000-0002-8515-4759 (2023) Design of an intelligent system for diabetes prediction by integrating rough set theory and genetic algorithm. In: Bagga, Teena, Upreti, Kamal, Kumar, Nishant, Ansari, Amirul Hasan and Nadeem, Danish, (eds.) Designing Intelligent Healthcare Systems, Products, and Services Using Disruptive Technologies and Health Informatics. CRC Press, Taylor Francis, Boca Raton, pp. 157-172. ISBN 978-1003217107 (In Press) (doi:https://doi.org/10.1201/9781003217107)

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Abstract

Diabetes causes a lot of damage to the internal organs and body parts. To find out the primary symptoms or features to detect and prevent the disease from its severity, an intelligent healthcare system is needed to analyse the disease data to save the patients with proper medication beforehand. The modern healthcare system requires proper IT solutions to process the medical data in disease management for diagnosis and prevention. Data analytics in the healthcare field demands both medical expertise and IT expertise. The feature selection process selects the important features from the feature pool and is generally applied as a pre-processing step prior to extracting the interesting classification rules from the data. In the paper, a rough set theory-based genetic algorithm (GA) method is proposed to select optimal feature set (called reduct)/symptoms from the diabetes dataset to predict the disease efficiently. Benchmark disease datasets are collected from the UCI repository. The experimental result shows that the selected features are important to predict the diabetes by providing good classification accuracy on existing benchmark classifiers, which proves the efficiency of the method.

Item Type: Book Section
Uncontrolled Keywords: rough set theory; genetic algorithm; intelligent system
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QH Natural history > QH426 Genetics
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENG)
Faculty of Engineering & Science > Wolfson Centre for Bulk Solids Handling Technology
Last Modified: 08 Aug 2022 15:15
URI: http://gala.gre.ac.uk/id/eprint/36777

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