Impact of Implementing Data Balancing Method in Intelligent Thyroid Cancer Detection

Recently, artificial intelligence became an alternative powerful solution in medical cases that assist the medical personnel to be a second opinion in making diagnosis decisions. In thyroid cancer cases, the development of an intelligent system offered valuable benefits since thyroid examination pro...

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Bibliographic Details
Main Authors: Nugroho, H.A., Frannita, E.L.
Format: Conference or Workshop Item
Published: 2021
Subjects:
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author Nugroho, H.A.
Frannita, E.L.
author_facet Nugroho, H.A.
Frannita, E.L.
author_sort Nugroho, H.A.
collection UGM
description Recently, artificial intelligence became an alternative powerful solution in medical cases that assist the medical personnel to be a second opinion in making diagnosis decisions. In thyroid cancer cases, the development of an intelligent system offered valuable benefits since thyroid examination procedures highly depended on the medical personnel skills and experiences. However, the number of training data still became a challenge in the development of the intelligent system. In this study, we developed an intelligent classification method for thyroid cancer completed with a data balancing method. Our proposed solution aimed to maintain the model performance in a small dataset. Our proposed solution successfully increased the classification performance with an average increasing performance of more than 60. While, the highest accuracy was 87.20. This experimental result indicated that implementing data balancing method can significantly increase the classification performance. © 2021 IEEE.
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spelling oai:generic.eprints.org:2804172023-11-10T03:02:47Z https://repository.ugm.ac.id/280417/ Impact of Implementing Data Balancing Method in Intelligent Thyroid Cancer Detection Nugroho, H.A. Frannita, E.L. Cancer Diagnosis Recently, artificial intelligence became an alternative powerful solution in medical cases that assist the medical personnel to be a second opinion in making diagnosis decisions. In thyroid cancer cases, the development of an intelligent system offered valuable benefits since thyroid examination procedures highly depended on the medical personnel skills and experiences. However, the number of training data still became a challenge in the development of the intelligent system. In this study, we developed an intelligent classification method for thyroid cancer completed with a data balancing method. Our proposed solution aimed to maintain the model performance in a small dataset. Our proposed solution successfully increased the classification performance with an average increasing performance of more than 60. While, the highest accuracy was 87.20. This experimental result indicated that implementing data balancing method can significantly increase the classification performance. © 2021 IEEE. 2021 Conference or Workshop Item PeerReviewed Nugroho, H.A. and Frannita, E.L. (2021) Impact of Implementing Data Balancing Method in Intelligent Thyroid Cancer Detection. In: 2021 International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE). https://ieeexplore.ieee.org/document/9649624
spellingShingle Cancer Diagnosis
Nugroho, H.A.
Frannita, E.L.
Impact of Implementing Data Balancing Method in Intelligent Thyroid Cancer Detection
title Impact of Implementing Data Balancing Method in Intelligent Thyroid Cancer Detection
title_full Impact of Implementing Data Balancing Method in Intelligent Thyroid Cancer Detection
title_fullStr Impact of Implementing Data Balancing Method in Intelligent Thyroid Cancer Detection
title_full_unstemmed Impact of Implementing Data Balancing Method in Intelligent Thyroid Cancer Detection
title_short Impact of Implementing Data Balancing Method in Intelligent Thyroid Cancer Detection
title_sort impact of implementing data balancing method in intelligent thyroid cancer detection
topic Cancer Diagnosis
work_keys_str_mv AT nugrohoha impactofimplementingdatabalancingmethodinintelligentthyroidcancerdetection
AT frannitael impactofimplementingdatabalancingmethodinintelligentthyroidcancerdetection