Use of Machine Learning Techniques for Diagnosis of Thyroid Gland Disorder

Abstract

The advancements of computer technologies have generated an incredible amount of data and information from numerous sources. Nowadays the way of implementing health care are being changing by utilizing the benefits of advancements in computer technologies. it is believed that engineering this amount of data can assist in developing predictive tool that can help physicians to diagnosing and predicting some debilitating life-threatening illness such as thyroid gland disease. Our current work focuses on investigating python languages to diagnose thyroid gland disease based on machine learning and involves developing a new tool to predict the diagnoses of thyroid gland diseases which we have called as a MLTDD (Machine Learning App for thyroid Disease Diagnosis). MLTDD has been designed with Qt designer and programmed using PyDev which is python iDE for Eclipse. MLTDD could diagnose with 99.81% accuracy. Decision tree algorithm has been used to create the ML model in addition to training dataset to learn from. ML model can be used to get predictions on new data for which you do not know the target and that is what we did to predict the diagnosis of thyroid gland disease as a hyperthyroidism or hypothyroidism or a normal condition using CRT decision tree algorithm. MLTDD can minify the cost the waiting time and help physicians for more research as well as decrease the errors and mistakes that can be made by humans on account of exhaustion and tiredness.