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The “Thyroid Diagnosis System” uses the XGBoost classifier algorithm of machine learning to help with thyroid diagnosis. For diagnosing and getting the results, it takes factors like age, sex, thyroxine use or not, goiter issue, tumor issue and hormonal level values such as T3, TSH,T4 and FTI and also other demographic values. These inputs are taken by the system to diagnose patient and gives result as three types: hypothyroidism, hyperthyroidism and no thyroid disorder. The collected sample dataset is split 80% for training the XGBoost classifier and 20% for testing its performance respectively. During training, it identifies patterns and relationships of data in dataset to ensure to get accurate predictions using given features.

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Thyroid Diagnosis Using Machine Learning Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Jenita Subash,
  • S. Sivaramakrishnan,
  • K. Pramilarani,
  • P. Tejaswini Reddy,
  • Madhushree Ontagodi,
  • R. Keerthana,
  • Kanishk Sanadi,
  • SriHarshini Devi Kaipa

摘要

The “Thyroid Diagnosis System” uses the XGBoost classifier algorithm of machine learning to help with thyroid diagnosis. For diagnosing and getting the results, it takes factors like age, sex, thyroxine use or not, goiter issue, tumor issue and hormonal level values such as T3, TSH,T4 and FTI and also other demographic values. These inputs are taken by the system to diagnose patient and gives result as three types: hypothyroidism, hyperthyroidism and no thyroid disorder. The collected sample dataset is split 80% for training the XGBoost classifier and 20% for testing its performance respectively. During training, it identifies patterns and relationships of data in dataset to ensure to get accurate predictions using given features.