article · IEEE Access
Accurate detection of thyroid disease using machine learning is frequently limited by data imbalance and biases inherent to individual models. To overcome these constraints, a diagnostic framework integrates filter-based feature selection with a stacking ensemble of several base models. By combining predictions across diverse algorithms, the system capitalises on their complementary strengths to boost overall predictive performance. The use of feature selection also lowers screening time and expenses by relying on fewer clinical attributes for assessment. Evaluated across extensive experiments on a clinical thyroid disease dataset, the ensemble framework achieved a receiver operating characteristic area under the curve score of 99.9 percent. These results demonstrate that moving away from individual algorithms towards collective ensemble architectures provides a more robust and accurate method for clinical thyroid disease screening.
Thyroid disorders affect millions globally, yet accurate clinical diagnosis can be slowed down by complex testing procedures and inconsistent algorithmic tools. By relying on fewer clinical attributes while achieving high predictive accuracy, this ensemble approach points towards faster, cheaper, and more dependable screening tools for healthcare providers, potentially reducing diagnostic delays and lowering diagnostic costs for patients.
This framework offers potential applications in clinical decision-support software for healthcare practitioners conducting thyroid disease screening. By using fewer clinical variables, it could lower testing expenses for diagnostic laboratories and clinics. Because the system has been evaluated on a retrospective clinical dataset, it represents applied and tested research that would require prospective clinical trials and regulatory approval before real-world deployment.
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In recent years, machine learning (ML) has become a pivotal tool for predicting and diagnosing thyroid disease. While many studies have explored the use of individual ML models for thyroid disease detection, the accuracy and robustness of these single-model approaches are often constrained by data imbalance and inherent model biases. This study introduces a filter-based feature selection and stacking-based ensemble ML framework, tailored specifically for thyroid disease detection. This framework capitalizes on the collective strengths of multiple base models by aggregating their predictions, aiming to surpass the predictive performance of individual models. Such an approach can also reduce screening time and costs considering few clinical attributes are used for diagnosis. Through extensive experiments conducted on a clinical thyroid disease dataset, the filter-based feature selection approach and the ensemble learning method demonstrated superior discriminative ability, reflected by improved receiver operating characteristic-area under the curve (ROC-AUC) scores of 99.9%. The proposed framework sheds light on the complementary strengths of different base models, fostering a deeper understanding of their joint predictive performance. Our findings underscore the potential of ensemble strategies to significantly improve the efficacy of ML-based detection of thyroid diseases, marking a shift from reliance on single models to more robust, collective approaches.
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DOI: 10.1109/access.2024.3418974
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