Development of a Diabetes Mellitus Diagnostic System Using Self-Organizing Map Algorithm: A Machine Learning Approach

Abstract

Delivery of Health care services in developing nations has posed a huge problem to the world at large. The United Nations and the World Health Organization have been on the front burner sorting for ways of improving these problems to abate the yearly mortality rates which are caused largely by inadequate health facilities, poor technical know-how, and poor health care administration. One disease that has a high number of patients is diabetes. In Nigeria, out of a population of 200 million, diabetes kills over 2% yearly. To reduce this menace, early diagnosis and awareness are important. And automation of the medical diagnostic system is one of the sure ways of achieving these feet. This paper explores the potential of a self-organizing map algorithm; a machine learning technique in the development of a diabetes mellitus diagnostic system (DMDS). Data collected from 120 patients from the University of Port Harcourt Teaching Hospital (UPTH) was used in the training and validation of the model. The confusion matrix formula was used in testing the sensitivity and accuracy of the model which yielded 75.63% and 87.2% respectively which are within the accepted range, predefined by expert physicians. Keywords Artificial Intelligence, self-organizing map, diagnosis, neural networks, diabetes mellitus