A Multi-layer perceptron based intelligent thyroid disease prediction system

Abstract

A challenging task for the modern research is to accurately diagnose the diseases prior to their treatment. Particularly in rural areas, the instant diagnosis for a life style disease is rarely available; it becomes necessary to use modern computing techniques to design intelligent prediction systems. A machine learning model is used for solving complex and non-separable prediction problems in different fields like medical diagnosis, decision support systems, biochemical analysis, image processing and financial analysis etc. The accuracy for thyroid diagnosis system may be improved by considering few additional attributes like heredity, age, anti-bodies etc. In this paper, an improved and intelligent thyroid disease prediction system is developed using multilayer perceptron (MLP) machine learning model. The proposed system uses 7 to 11 features of the individuals to classify them in normal, hyperthyroid and hypothyroid classes. The system uses gradient descent backpropogation algorithm for training the machine learning model using dataset of 120 subjects collected from SKIMS Hospital, Jammu and Kashmir. The thyroid prediction system promises excellent overall accuracy of nearly 99.8% for 11 attributes with more number training instances. However, the system results in a lower accuracy of 66.7% using 11 attributes and 70% using 7 attributes with 30 subjects.