article · Egyptian Journal of Artificial Intelligence
Thyroid disease affects populations worldwide, making timely and accurate clinical diagnosis essential. Artificial intelligence provides modern approaches to support medical information systems in offering individualised diagnosis and therapy planning. To address this, an optimised multi-class classification framework using the XGBoost algorithm was developed to classify patients across three distinct thyroid diseases. The method enhances feature selection accuracy from raw data and fine-tunes hyperparameters to maximise predictive performance. Using a benchmark thyroid disease dataset from the UCI machine learning repository for training and evaluation, the optimised model demonstrated superior performance and recall compared with state of the art approaches. It achieved a classification accuracy of 99 percent, highlighting its effectiveness for automated medical data analysis.
Thyroid disorders are increasingly prevalent across the globe, requiring dependable tools to assist medical staff with timely diagnosis. Employing high-accuracy machine learning algorithms within healthcare information systems can improve the classification of complex conditions, helping clinicians deliver more accurate, personalised treatment plans for patients.
The algorithm could be incorporated into medical diagnostic software and clinical information systems to assist healthcare practitioners in diagnosing thyroid conditions. Judged strictly on the abstract, the research is at an applied and tested stage using a retrospective UCI benchmark dataset, meaning further clinical validation would be needed before practical deployment.
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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 multi-class 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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DOI: 10.21608/ejai.2023.205554.1008
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