Thyroid data analysis using Proximity Relative Compressed Algorithm for Health Care Applications

Abstract

Data Analysis is popular in various fields of health data. This study's purpose is to analyze the characteristics of the medical system, applications, analytical methods, and medical information. Machine learning is one of the main processing methods in all walks of our life. In this article, investigation scholars wish to work in thyroid disease prediction through a reference source. Two most popular thyroid diseases are classified in the current research of thyroid dysfunction i.e. hyperthyroidism and hypothyroidism. The author analyzes the naive Bayes models, decision trees, and perceptron’s and radial basis function networks to compare the four categories. The above models' classification has produced a remarkable precision of the results.Compared to other models, decision tree is the best classification model.With this excellent classification of recycled logs and adjacent data, the Proximity Relative Compressed Algorithm (PRCA) implements support using Python-based organization.