Multi-channel deep convolutional neural networks for multi-classifying thyroid disease

Zhang, Xinyu; Lee, Vincent CS.; Jia, Ru; Lee, James C.; Song, Jiangning; Liu, Feng

doi:10.48550/arxiv.2203.03627

Cited by 1 publication

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“…Different thyroid conditions can cause weight loss, weight gain, anxiety, irritability, eyesight issues, intolerance to cold or hot temperatures, and other symptoms that have a significant influence on the human body [6]. Since the 1990s, there have been an increasing number of thyroid illness cases, and in recent years, thyroid cancer has become the malignancy with the fastest rate of increase [7].…”

Section: Introduction 11 Overviewmentioning

confidence: 99%

Thyroid Disease Multi-class Classification based on Optimized Gradient Boosting Model

Alnaggar

Handosa

Medhat

et al. 2023

Egyptian Journal of Artificial Intelligence

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Human healthcare is one of the most important issues in society to ensure that patients receive the care they require as quickly as possible. One of the disorders that affects the global population and is becoming more prevalent is thyroid disease. Medical information systems are crucial in their capacity to diagnose thyroid disease. Artificial intelligence has recently offered fresh approaches to the existing clinical treatment issues and has demonstrated promising results for individualized diagnosis and therapy planning. Hence, this paper proposes an optimized multiclass classification model, which depends on XGBoost to classify patients with different types of thyroid disease. The main contribution is to (i) propose a Multiclass-Classification for the purpose of diagnosing three different thyroid diseases, (ii) raise the row dataset's feature selection accuracy for classification. (iii) utilize the highly selective XGBoost algorithm for the chosen characteristics, (iv) show that The XGBoost has the best performance and recall, making it the top choice for data analysis in terms of classifying thyroid disease, and (v) improve upon findings from earlier studies by doing the proposed study. XGBoost is trained and tested using UCI machine learning repository dataset of thyroid disease. In addition to build the model with the optimized hyperparameters to achieve and compare the gained results aiming to get the best score of accuracy. From the results, it is shown that the optimized XGBoost achieved 99% accuracy as a win over performing compared with the state of arts models.

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Section: Introduction 11 Overviewmentioning

confidence: 99%