Prediction of Thyroid Disease Based on Machine Learning Algorithms

Accurately diagnosing thyroid disease remains a challenge in medicine, necessitating the use of reliable computational techniques for early detection and categorization. Using a dataset of 3164 patient records, this study applies machine learning algorithms to create a computer-aided thyroid disease diagnosis system. The data set includes demographic information, clinical indicators, and thyroid function test results. To improve model performance, the study focuses on preprocessing approaches such as data cleaning, normalization, and feature selection. The use of feature engineering techniques to determine the most predictive characteristics associated with thyroid problems is highlighted. Five machine learning classifiers—Random Forest, K-Nearest Neighbors, Support Vector Machines, Decision Tree, and Logistic Regression—are evaluated and compared using stratified k-fold cross-validation. Performance metrics, such as precision, recall, and F1-score, are used to assess the effectiveness of each model in predicting hypothyroidism based on clinical attributes. Accurate diagnosis of thyroid disease remains a challenge in medicine, necessitating the use of dependable computational techniques for early detection and categorization. Using a dataset of 3164 patient records, this study employs machine learning algorithms to develop a computer-aided thyroid disease diagnosis system.