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- W4285020462 abstract "Today, machine learning plays a significant role in the classification of healthcare conditions. Machine learning is the process of finding, discovering, and modeling massive amounts of data in order to identify unknown relationships and patterns that are useful to the decision makers. Medical data mining has begun to emerge exemplary, with the potential to discover interesting insights from medical domain datasets. These patterns may be used in the making of clinical decisions. In this research work, we proposed genetic algorithm and Naïve Bayes-based (GANB) diabetes mellitus prediction system. In which, genetic algorithm (GA) is used for feature selection and Naïve Bayes is used for prediction. The proposed system is trained using Pima Indian diabetes dataset (PIDD). The dataset is preprocessed using synthetic minority oversampling technique (SMOTE) for solving the issue of class imbalances. The effectiveness of the system is evaluated using classification accuracy (CA) and error. The simulation results show that the proposed GANB system achieves better accuracy as compared to the results of related earlier studies." @default.
- W4285020462 created "2022-07-12" @default.
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- W4285020462 date "2022-07-12" @default.
- W4285020462 modified "2023-09-25" @default.
- W4285020462 title "Genetic Algorithm and Naïve Bayes-Based (GANB) Diabetes Mellitus Prediction System" @default.
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- W4285020462 doi "https://doi.org/10.1007/978-981-19-1906-0_47" @default.
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