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Analysis of Decision Tree Algorithms for Diabetes Prediction

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Business Intelligence (CBI 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 449))

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Abstract

Data Mining (DM) is a helpful tool to extract and exploit the information from a large data set. There are different methods and algorithms available in data mining field. Several DM algorithms are used for classification such as Artificial Neural Network (ANN), K-Nearest Neighbor (K-NN), etc. The Decision Tree (DT) mining remains the best algorithm. In this paper, different classification methods including decision tree, C-RT, C5.0, AD-Tree and CS-MC4 algorithms are presented. These algorithms are evaluated using Recall, precision and F-measure. Experimental results show that AD-Tree is faster and present higher accuracy than the other classifier using a Diabetes data set.

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Authors and Affiliations

  1. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Youssef Fakir & Naoum Abdelmotalib

Authors
  1. Youssef Fakir
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  2. Naoum Abdelmotalib
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Correspondence to Youssef Fakir .

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Editors and Affiliations

  1. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Mohamed Fakir

  2. Sultan Moulay Slimane University, Beni Mellal, Morocco

    Mohamed Baslam

  3. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Rachid El Ayachi

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Fakir, Y., Abdelmotalib, N. (2022). Analysis of Decision Tree Algorithms for Diabetes Prediction. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2022. Lecture Notes in Business Information Processing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-06458-6_16

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  • DOI: https://doi.org/10.1007/978-3-031-06458-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06457-9

  • Online ISBN: 978-3-031-06458-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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