Date of Award


Document Type


Degree Name

Master of Science in Computer Science


School of Computer Science and Engineering

First Reader/Committee Chair

Dr.Qingquan Sun


Thyroid illness frequently manifests as hypothyroidism. It is evident that people with hypothyroidism are primarily female. Because the majority of people are unaware of the illness, it is quickly becoming more serious. It is crucial to catch it early on so that medical professionals can treat it more effectively and prevent it from getting worse. Machine learning illness prediction is a challenging task. Disease prediction is aided greatly by machine learning. Once more, unique feature selection strategies have made the process of disease assumption and prediction easier. To properly monitor and cure this illness, accurate detection is essential. In order to build models that can forecast the development of hypothyroidism. In this project, we utilized machine learning approaches such Logistic Regression, Decision Trees, and Naive Bayes. Here we used thyroid function-related measures and characteristics from a UCI Machine Learning Repository dataset. The main goals were to properly assess each machine learning model's performance and fine-tune its hyperparameters. With an accuracy rate of 99.87%, the findings of this study generated the model's ability to predict hypothyroidism were pretty remarkable. This high degree of accuracy shows how useful these machine learning algorithms are as diagnostic v and therapeutic tools for hypothyroid patients early on. This experiment demonstrates the potential of machine learning in healthcare and has an impact on diagnosis. It is crucial that you do this appropriately.