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- W4375842983 abstract "Binary classification of Thyroid has been observed in several manuscripts in literature. This study also classifies the disease using twelve machine learning algorithms. The results are compared with existing proposals and found to exhibit better performance in terms of the standard machine learning algorithm evaluation parameters. The result improvements are due to the data preprocessing techniques that have been applied in the study. The results that are presented in this paper are void of any tuned machine learning model. Only the basic implementations have been done on the preprocessed data. Random Forest presented the best results amongst all the participants, including literary counterparts with an accuracy of 99.14%, coupled with an F1-Score of 0.991. We have implemented the machine learning models on a resource constraint dataset containing only 3772 instances, and an initial feature vector containing 29 features. Data preprocessing minimized the initial feature vector to a reduced feature vector containing 15 features. We claim this study can form a benchmark for binary classification of thyroid using machine learning models, as our results exhibit implementation on twelve machine learning models as well as improvements in the performances." @default.
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- W4375842983 date "2023-01-01" @default.
- W4375842983 modified "2023-09-25" @default.
- W4375842983 title "Binary Classification of Thyroid Using Comprehensive Set of Machine Learning Algorithms" @default.
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- W4375842983 doi "https://doi.org/10.1007/978-981-19-5191-6_22" @default.
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