Predictive Modeling Of Thyroid Cancer Risk And Recurrence Using Machine Learning Techniques In Otolaryngology (Ent)
Keywords:
Thyroid cancer, machine learning, predictive modelling, reliability analysis, PCA, Shapiro-Wilk test, otolaryngology, AI in health, questionnaire validation, Cronbach’s AlphaAbstract
Background: The use of primary options of otolaryngology (ENT) in diagnosis and prognostication of thyroid cancer is a cause of concern. Predictive modelling of machine learning (ML) can help to enhance early detection, risk stratification, or recurrence prediction. This paper would hypothesize and verify the statistical significance of structured data that can be utilized with ML in the prediction of any cancer caused in the thyroid.
Purpose: The assessment of normality, reliability, and validity of the questionnaire-based data referring to the demographic, clinical, diagnostic, and behavioural variables that can be applied to the outcomes of the thyroid cancer and the attitude to the machine learning tool.
Procedures: The highest number of patients was simulated, 273, through the use of a standard questionnaire covering various areas: demographics, thyroid function tests, histopathological analysis, and through the use of Likert scales to capture the patient attitudes. The statistical tests that were used here were Cronbach's alpha, which showed internal consistency; Shapiro-Wilk, which showed the normality; and PC analysis, which showed the construct validity.
Results: Shapiro-Wilk test showed that most of the variables were of the continuous type; most of them were normally distributed (p > 0.05) and, thus, could be studied using parametric methods. The Likert-scale component possesses the Cronbach Alpha of 0.909, and this indicates that it is a good indicator will internal consistency and reliability. The PCA showed that it was unidimensional, and the scale has construct validity since the three items of attitudinal measures were loaded at a higher degree on a single key factor.
Conclusion: The data has been statistically validated, and it is very reliable and constructively validated, and hence quite usable to be a component of the machine learning predictive modelling. The outcomes show that there is a chance to advance the means of managing thyroid cancer with the help of AI that comprises both clinical and attitude characteristics in ENT practice.
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