THYROID DISEASE CLASSIFICATION ANALYSIS USING XGBOOST MULTICLASS

Authors

  • haris samuel pranada panjaitan a:1:{s:5:"en_US";s:27:"Universitas Prima Indonesia";}
  • Agustinus Gulo Universitas Prima Indonesia Fakultas Teknologi Dan ilmu Komputer Program Studi Sistem Informasi
  • Ahmad Haikal Alfi Universitas Prima Indonesia Fakultas Teknologi Dan ilmu Komputer Program Studi Sistem Informasi
  • Okta Jaya Harmaja Universitas Prima Indonesia Fakultas Teknologi Dan ilmu Komputer Program Studi Sistem Informasi
  • Evta Indra Universitas Prima Indonesia Fakultas Teknologi Dan ilmu Komputer Program Studi Sistem Informasi

DOI:

https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2831

Keywords:

Klasifikasi, Tiroid, Xgboost Multiclass, Machine Learning

Abstract

ABSTRAK- Sickness is an unusual condition of the body or mind that causes discomfort, malfunction, or suffering to the sick person. One disorder that occurs due to a lack of health concerns is thyroid disease. The thyroid is a butterfly-shaped endocrine gland near the neck's bottom. The diagnosis of thyroid disease is complicated because the symptoms of thyroid disease can fluctuate based on the rise and fall of thyroid hormones, which increase the utilization of oxygen by the body's cells. In this case, a thyroid examination by a doctor and proper interpretation of clinical data is required to identify thyroid disease. However, the limitations of a doctor due to age and time constraints lead to a lack of interpretation of patient clinical data. Therefore, a study was conducted on the analysis of thyroid disease classification to simplify and speed up the process of diagnosing thyroid disease using the Xgboost Multiclass method, which is expected to get an accuracy value above 90%.

Keywords: Classification, Thyroid, Xgboost Multiclass, Machine Learning

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Published

2022-09-29

How to Cite

[1]
haris samuel pranada panjaitan, A. Gulo, A. H. . Alfi, O. J. Harmaja, and E. Indra, “THYROID DISEASE CLASSIFICATION ANALYSIS USING XGBOOST MULTICLASS”, JUSIKOM PRIMA, vol. 6, no. 1, pp. 105-110, Sep. 2022.

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