Thyroid Disease Prediction Using Machine Learning Model: Decision Tree

Abstract

Thyroid illness must be correctly classified in order to have an accurate diagnosis and effective therapy. Diagnostic accuracy and efficiency have both been found to improve with the use of machine learning methods. The decision tree algorithm has been shown to be useful for healthcare classification issues. Using a dataset of 793 patients, this investigation investigates the utility of the decision tree method for classifying thyroid illnesses. The results show that the decision tree algorithm has high rates of accuracy, recall, and precision for classifying thyroid diseases. The decision tree approach is simple, can deal with missing values and noisy data, and is straightforward to comprehend when used to the categorization of thyroid diseases. This research recommends feature selection, hyperparameter adjustment, and efficient data preparation as means to enhance the efficiency of the decision tree method. The precision and dependability of machine learning models for diagnosing thyroid diseases may be further enhanced by cooperation between data scientists and healthcare practitioners. This study finds that the decision tree algorithm is a valuable resource for classifying thyroid diseases and suggests further investigation into areas including algorithm integration, bigger studies, and real-time diagnosis.