Machine Learning Approaches for Early Diagnosis of Thyroid Cancer

EasyChair Preprint 13648

Abstract

Thyroid cancer is a prevalent form of cancer that requires early detection for effective treatment and improved patient outcomes. Conventional diagnostic methods for thyroid cancer have limitations in terms of accuracy and efficiency. Machine learning approaches have emerged as promising tools in the early diagnosis of thyroid cancer. This abstract provides an overview of the application of machine learning in thyroid cancer diagnosis.

Machine learning algorithms can analyze large volumes of patient data, including medical images, genomic data, and clinical information, to identify patterns and predict the presence of thyroid cancer. These algorithms can be trained on labeled datasets, where the features are extracted from the data, or they can learn from unlabeled data using unsupervised techniques. Supervised learning algorithms such as Support Vector Machines, Random Forests, and Neural Networks have shown promise in accurately classifying thyroid cancer cases.

Data acquisition and preprocessing play a crucial role in machine learning approaches. Relevant medical data must be collected, and preprocessing techniques are applied to handle missing data and outliers. Feature selection and extraction methods are employed to identify the most informative features that contribute to the accurate diagnosis of thyroid cancer.

Keyphrases: Accuracy, Artificial Intelligence, early diagnosis, machine learning, performance metrics

@booklet{EasyChair:13648,
  author    = {Elizabeth Henry},
  title     = {Machine Learning Approaches for Early Diagnosis of Thyroid Cancer},
  howpublished = {EasyChair Preprint 13648},
  year      = {EasyChair, 2024}}