article · Discover Applied Sciences
Thyroid disease represents a widespread endocrine disorder that relies on early and precise identification for successful medical intervention. Machine learning techniques provide a computational approach to improve the diagnosis and categorisation of such conditions. By utilising patient datasets featuring demographic details, age, gender, and hormone levels, predictive models were constructed and evaluated. The methods analysed include random forest, support vector machines, XGBoost, and ensemble classifiers to distinguish between conditions such as hypothyroidism and hyperthyroidism. Model development encompassed standard data preparation, feature selection, hyperparameter adjustment, and cross-validation procedures. Performance assessment measured standard statistical metrics including precision, recall, accuracy, F1-score, voting approaches, and the area under the receiver operating characteristic curve to determine comparative algorithmic capability.
Thyroid conditions affect millions of individuals across the globe, making prompt and accurate detection crucial for proper clinical management. Developing robust computational models using routine indicators such as hormone concentrations and demographic data helps benchmark diagnostic tools that could support clinicians in identifying distinct thyroid disorders more reliably.
The work explores predictive algorithms that could eventually inform clinical decision-support software for healthcare providers diagnosing thyroid conditions. However, because the study focuses on model comparison and evaluation methodologies without reporting clinical integration or real-world trials, the technology remains at an early stage of research.
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Abstract A common endocrine issue affecting millions globally is thyroid illness. For this ailment to be effectively treated and managed, an early and accurate diagnosis is essential. Machine learning algorithms have attracted a lot of attention recently in the healthcare industry and have the potential to improve thyroid disease diagnosis and categorization. The implementation of machine learning methods for the classification of thyroid disease is presented in this study. To create predictive models, the study makes use of a dataset that includes a variety of thyroid-related factors, including age, gender, and hormone levels. To evaluate the effectiveness of several machine learning techniques in classifying thyroid diseases, including random forest, support vector machines, XG-Boost, and ensemble classifier, they are implemented and compared. To ensure robust model performance, the methodology includes data preparation, feature selection, and model training, as well as strategies for hyperparameter adjustment and cross-validation. To assess the algorithms’ efficiency in differentiating between several thyroid illness classifications, such as hyperthyroidism, hypothyroidism, and the study measures the algorithms’ accuracy, precision, recall, F1-score, voting, and area under the ROC curve.
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DOI: 10.1007/s42452-024-06068-w
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