An Innovative Machine Learning Approach for Early Detection of Thyroid Disorders
Keywords:
SVM, DT, RF, LR, NB, Machine learning, Thyroid diseases predictionAbstract
Thyroid diseases are among the most prevalent endocrine disorders worldwide, necessitating accurate and timely diagnosis to ensure effective treatment and management. This study investigates the application of machine learning models for predicting thyroid diseases, utilizing a dataset comprising clinical and demographic attributes. Models such as Logistic Regression (LR), Decision
Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were evaluated across datasets of varying sizes. A Proposed Method was developed and tested, demonstrating consistent superiority over traditional models with an accuracy of 97% for the largest dataset.
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