A Machine Learning Approach to Predict Thyroid Disease at Early Stages of Diagnosis

Abstract

Classification based Machine learning plays a major role in various medical services. In medical field, the salient and demanding task is to diagnose patient's health conditions and to provide proper care and treatment of the disease at the initial stage. Let us consider Thyroid disease as the example. The normal and traditional methods of thyroid diagnosis involve a thorough inspection and also various blood tests. The main goal is to recognize the disease at the early stages with a very high correctness. Machine learning techniques play a major role in medical field for making a correct decision, proper disease diagnosis and also saves cost and time of the patient. The purpose of this study is prediction of thyroid disease using classification Predictive Modelling followed by binary classification using Decision Tree ID3 and Naive Bayes Algorithms. The Thyroid Patient dataset with proper attributes are fetched and using the Decision Tree algorithm the presence of thyroid in the patient is tested. Further, if thyroid is present then Naïve Bayes algorithm is applied to check for the thyroid stage in the patient.

Ähnliche Arbeiten