Thyroid Disease Classification using Machine Learning

Abstract

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The increasing prevalence of thyroid disorders necessitates an efficient and reliable system for early diagnosis and classification. Machine learning (ML) offers a promising approach to predict thyroid conditions by analyzing clinical data effectively. This study focuses on developing a robust thyroid classification system using four machine learning algorithms: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The system leverages a publicly available thyroid dataset, consisting of various clinical parameters, for preprocessing and model training. The ML pipeline begins with data preprocessing techniques such as handling missing values, normalization, and feature selection to ensure the quality of input data. Logistic Regression, as a baseline model, provides interpretability and initial insights into the dataset. Random Forest, a robust ensemble algorithm, is employed for its capability to handle high-dimensional data and prevent over fitting. Support Vector Machine, with its kernel-based approach, demonstrates high performance in identifying non-linear relationships within the data. K-Nearest Neighbors is utilized for its simplicity and effectiveness in capturing local patterns. Extensive experimentation is conducted to evaluate the models using metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). Results reveal that Random Forest outperforms other algorithms in terms of accuracy and recall, demonstrating its suitability for thyroid classification. However, SVM also achieves competitive results, particularly for imbalanced data scenarios. The study highlights the importance of feature selection and hyper parameter tuning to optimize model performance.