Predictive Modeling for Thyroid Disease Diagnosis using Machine Learning

Thyroid "conditions pose a worldwide health challenge, demanding precise and timely assessments. This research embarks on a three-stage investigation into machine learning (ML) algorithms for diagnosing thyroid diseases. Initially, the dataset's imbalance is rectified using Synthetic Minority Over-sampling Technique (SMOTE). Following this, models such as SVM, K-NN, Decision Trees, Random Forest, Naive Bayes, and Extra Trees undergo evaluation for predictive effectiveness. Metrics including accuracy, precision, recall, and F1 score are computed to gauge the efficiency of each algorithm. The results provide valuable insights into the optimal ML approach for thyroid disease diagnosis, contributing to the advancement of accurate clinical decision support systems in healthcare.