B Sai Lokesh, M. Anusha 03 07 Adaptive Intelligence for Early Thyroid Disease Detection and Classification presents an intelligent decisionsupport system designed to assist in the early identification of thyroid disorders using machine learning techniques The proposed application provides separate modules for administrators and users enabling efficient dataset management model training and disease prediction through a userfriendly interface The thyroid dataset contains multiple clinical attributes associated with three diagnostic categories Normal Hyperthyroid and Hypothyroid Before model development the data undergoes preprocessing including cleaning label encoding and feature transformation to ensure compatibility with machine learning algorithms Several classification models including Decision Tree Random Forest Support Vector Machine KNearest Neighbors Logistic Regression Nave Bayes and MultiLayer Perceptron are trained and evaluated using performance measures such as accuracy precision recall and F1score Comparative analysis identifies Random Forest and Decision Tree as the most effective models for thyroid disease classification The system also allows users to predict thyroid conditions by entering clinical values or uploading test data Developed using Python Django and MySQL the proposed framework simplifies disease assessment supports early diagnosis reduces manual effort and provides a reliable platform for intelligent thyroid disease detection Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026