THYROID DISEASE CLASSIFICATION ANALYSIS USING XGBOOST MULTICLASS
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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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