Evaluation of Gradient Boosted Classifier in Atopic Dermatitis Severity Score Classification

Authors

  • Rivansyah Suhendra Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh Barat 23681, Indonesia
  • Suryadi Suryadi Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh Barat 23681, Indonesia
  • Noviana Husdayanti Teuku Umar Hospital, Aceh Jaya 23654, Indonesia
  • Aga Maulana Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Teuku Rizky Noviandy Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Novi Reandy Sasmita Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Muhammad Subianto Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Nanda Earlia Department of Dermatology and Venereology, Faculty of Medicine Universitas Syiah Kuala, Banda Aceh, Aceh, Indonesia; Department of Dermatology and Venereology, Dr. Zainoel Abidin Hospital, Banda Aceh, Aceh, Indonesia
  • Nurdjannah Jane Niode Department of Dermatology and Venereology, Faculty of Medicine, University of Sam Ratulangi, Manado, 95163, Indonesia
  • Rinaldi Idroes Department of Chemistry, Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/hjas.v1i2.85

Keywords:

Dermatology, Atopic dermatitis, Machine learning, Gradient boosted, Severity scoring

Abstract

This study investigates the application of the Gradient Boosting machine learning technique to enhance the classification of Atopic Dermatitis (AD) skin disease images, reducing the potential for manual classification errors. AD, also known as eczema, is a common and chronic inflammatory skin condition characterized by pruritus (itching), erythema (redness), and often lichenification (thickening of the skin). AD affects individuals of all ages and significantly impacts their quality of life. Accurate and efficient diagnostic tools are crucial for the timely management of AD. To address this need, our research encompasses a multi-step approach involving data preprocessing, feature extraction using various color spaces and evaluating classification outcomes through Gradient Boosting. The results demonstrate an accuracy of 93.14%. This study contributes to the field of dermatology by providing a robust and reliable tool to support dermatologists in identifying AD skin disease, facilitating timely intervention and improved patient care.

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Published

2023-09-23

How to Cite

Suhendra, R., Suryadi, S., Husdayanti, N., Maulana, A., Noviandy, T. R., Sasmita, N. R., Subianto, M., Earlia, N., Niode, N. J. and Idroes, R. (2023) “Evaluation of Gradient Boosted Classifier in Atopic Dermatitis Severity Score Classification”, Heca Journal of Applied Sciences, 1(2), pp. 54–61. doi: 10.60084/hjas.v1i2.85.

Issue

Vol. 1 No. 2 (2023): October 2023

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Articles

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Copyright (c) 2023 Rivansyah Suhendra, Suryadi Suryadi, Noviana Husdayanti, Aga Maulana, Teuku Rizky Noviandy, Novi Reandy Sasmita, Muhammad Subianto, Nanda Earlia, Nurdjannah Jane Niode, Rinaldi Idroes

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