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The classification of medical and botanical data through majority voting using artificial neural network

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Abstract

Data classification has many approaches in data mining and machine learning. The artificial neural network (ANN) is applied to classify the data that might belong to various domains like chemical, botanical, medical, spatial, textual, and image. In this work, an ANN technique is applied to the 7 Life sciences (botanical and medical) data sets extracted from public data repositories. Various optimization approaches like exhaustive validation, cross-validation, and multiple seeding are used to discover the most optimized networks for the given datasets. Finally, voting predicts the class where the whole dataset is used as a test set instead of folds. The results obtained by the proposed approach outperform other approaches on all the datasets. Cleveland’s heart, Statlog heart, Dermatology, Hepatitis, Seeds, Abalone and Vertebral Column data sets (all of UCI) after applying the voting showed the accuracy of 94.61%, 93.7%, 99.73%, 96.77%, 99.05%, 89.37% and 90.32% respectively. In the future deep neural network may be used to improve the results.

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Data availability

Data will be available on request to the corresponding author.

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Authors and Affiliations

  1. Department of Computer Applications, The Maharaja SayajiRao University of Baroda, Vadodara, 390002, India

    Kshitij Tripathi

  2. Department of Information Technology, S.P. College, Cluster University, Srinagar, 190001, India

    Fayaz Ahmed Khan, Akib Mohi Ud Din Khanday & Khair U. L. Nisa

  3. Department of Computer Science and Software Engineering-CIT, United Arab Emirates University, Al Ain, UAE

    Akib Mohi Ud Din Khanday

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  1. Kshitij Tripathi
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  2. Fayaz Ahmed Khan
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  3. Akib Mohi Ud Din Khanday
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  4. Khair U. L. Nisa
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Correspondence to Kshitij Tripathi.

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Tripathi, K., Khan, F.A., Khanday, A.M.U.D. et al. The classification of medical and botanical data through majority voting using artificial neural network. Int. j. inf. tecnol. 15, 3271–3283 (2023). https://doi.org/10.1007/s41870-023-01361-0

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