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International Conference on Intelligent and Fuzzy Systems

INFUS 2022: Intelligent and Fuzzy Systems pp 360–368Cite as

Applying Fuzzy Decision Tree Method for Hypertension Classification in Adolescent

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 504)

Abstract

As the number of hypertension patients in adolescents continues to grow in Banda Aceh, the local office of health services has a large amount of data on hypertensive medical records. However, these data are only to count the growth of patients and there is no model for using these data for developing an intelligence analysis tool. With applying the concept of fuzzy sets in artificial intelligence and deep learning, the fuzzy decision tree method was designed by combining the fuzzy set concept and decision tree approach. In this paper, we developed a model to determine the best number of rules based on the fuzzy decision tree model. In addition, we also determine the highest accuracy value to get the best model. We classified the data correspondent into two adolescent groups which are teenagers aged from 12–17 years old and 18–23 years old. The result shows that the model obtained 79 rules for respondent data aged 12–17 years old and 60 rules for respondent data aged 18–23 years old at the value of θr by 85% and the value of θn by 3% and 5%. The highest accuracy value obtained was 87.18% for respondent data aged 12–17 years old and 87.50% for respondent data aged 18–23 years old. The classification results show that 20% of respondents suffer from hypertension, 12% suffer from stage 1 hypertension, and 2% suffer from stage 2 hypertension.

Keywords

  • Fuzzy decision tree
  • Hypertension
  • Fuzzy sets

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Author information

Authors and Affiliations

  1. Department of Statistics, Universitas Syiah Kuala, Darussalam, 23111, Indonesia

    Hizir Sofyan, Elfayani Elfayani & Marzuki Marzuki

  2. Department of Mathematics, University of Syiah Kuala, Darussalam, 23111, Indonesia

    Azalya Rahmatika

  3. Department of Informatics, Universitas Syiah Kuala, Darussalam, 23111, Indonesia

    Irvanizam Irvanizam

Authors
  1. Hizir Sofyan
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  2. Elfayani Elfayani
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  3. Azalya Rahmatika
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  4. Marzuki Marzuki
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  5. Irvanizam Irvanizam
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Corresponding author

Correspondence to Azalya Rahmatika .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Prof. Cengiz Kahraman

  2. Department of Industrial Engineering, Galatasaray University, Istanbul, Turkey

    Prof. A. Cagri Tolga

  3. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Prof. Sezi Cevik Onar

  4. Department of Industrial Engineering, Yildiz Technical University, Istanbul, Turkey

    Prof. Selcuk Cebi

  5. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Prof. Basar Oztaysi

  6. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Prof. Irem Ucal Sari

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Sofyan, H., Elfayani, E., Rahmatika, A., Marzuki, M., Irvanizam, I. (2022). Applying Fuzzy Decision Tree Method for Hypertension Classification in Adolescent. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-031-09173-5_44

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