COMPARATIVE ANALYSIS OF THYROID DISEASE PREDICTION MODELS USING MACHINE LEARNING AND DATA MINING TECHNIQUES AND ENSEMBLE CLASSIFIERS
The paper presents a comprehensive investigation into the development of a machine learning model tailored for the prediction of thyroid diseases, underscoring the significance of accurate and timely diagnosis. Leveraging a publicly available dataset from the University of California, Irvine, the study utilizes 29 clinical attributes to construct a robust ML model capable of early symptom detection and thyroid disorder prognosis. It delves into feature analysis, data visualization techniques, and employs cross-validation and synthetic minority oversampling to counter overfitting. Through ensemble learning methods, the model's reliability is reinforced, showcasing high levels of accuracy, sensitivity, and specificity, thus demonstrating its potential for integration into real-time computer-aided diagnostic systems. The research underscores the transformative impact of ML in healthcare, particularly in advancing the management of thyroid-related health conditions.
Machine Learning, Thyroid Disease Prediction, Diagnosis, Dataset Analysis, Cross-validation, Synthetic Minority Oversampling, Ensemble Learning, Real-time Diagnostic Systems, Healthcare Management.
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