Thyroid cancer is among the most common endocrine system malignancies and can recur. This demonstrates how critical it is to accurately determine the likelihood of recurrence. Consequently, maximizing patient treatment regimens and formulating follow-up strategies depend heavily on accurately estimating the probability of disease recurrence. Machine learning (ML) is used to find patterns and predict subtleties in large data sets that traditional analytical techniques cannot. Utilizing these insights in clinical practice will help manage patients' treatment and monitoring plans more effectively. In this study, we train Data Mining (DM) models to predict recurrence status (No Recurrence, Recurrence Present) in patients diagnosed with thyroid cancer and classify the trained models. The thyroid dataset is reduced from 16 to 11 attributes using Boruta (Random Forest) variable selection algorithm. Thyroid cancer is analyzed with the supervised learning model, the Associative classification (IC) method. The Ameva discretization algorithm was used to improve the performance of the models and to improve the data distribution. Classification Based Association Rules (CBAR) and Regularized Class Association Rules (RCAR) algorithms were used to build the prediction models. The performance of CBAR and RCAR algorithms in accurately classifying patients diagnosed with thyroid cancer was compared and found to be 95.8% and 94.5%, respectively. CBAR algorithm is more effective than RCAR algorithm in correctly classifying patients diagnosed with thyroid cancer.

CLASSIFICATION OF THYROID CANCER DIAGNOSIS STATUS USING RELATIONAL CLASSIFICATION METHOD

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