Thyroid Disease Diagnosis: A Study on the Efficacy of Feature Reduction and Biomarker Selection in Artificial Neural Network Models

Abstract

This study employs ANN to enhance thyroid disease diagnosis while minimizing features and choosing the most biomarkers. The data were analyzed focusing on three key indicators of thyroid function: TSH, TT4, and FTI. All of these biomarkers are vital signs that reflect thyroid activity and are incorporated in ANN models. This is achievable by minimizing the number of features and there by the Billboard ANN models deliver high diagnostic accuracy and high computational effectiveness. Computing with this simplified dataset results in faster computation times while at the same time, maintaining a high degree of diagnostic accuracy. Thus, the profound features of TSH, TT4, and FTI as indices of thyroid disorders, as well as the introduction of these markers into simple diagnostic algorithms, are discussed. Hence this study supports the application of ANN models in medical diagnosis by adding to the existing proof to the strategy. The data suggest that the exclusion of features can enhance the speed and boost the time to obtain a precise result.These improvements could have significant implications for clinical practice, especially in enhancing the management and treatment of thyroid diseases, where precise and prompt diagnosis is essential.

Keywords

• Thyroid
• Disease Diagnosis
• Artificial Neural Networks
• Feature Reduction

References

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