A Machine Learning-Based Intelligent System for Predicting Diabetes

Nabila Khan, Mehedi Muaz, Anusha Kabir, Muhammad Islam

OAI: oai:igi-global.com:247455 • DOI: 10.4018/IJBDAH.2019070101

In this era of technological growth, the diagnosis of diseases and finding cures, personal health parameter management and predicting the possibility of susceptibility to some diseases have become accessible and easy. Although all over the world millions of people are falling victim to diabetes, in most of the cases they are not even aware of their situation due to the silent nature of diabetes. Therefore, the objective of this research is to propose an intelligent system based on a machine learning algorithm to improve the accuracy of predicting diabetes. To attain this objective, an algorithm was proposed based on Naïve Bayes with prior clustering. Second, the performance of the proposed algorithm was evaluated using 532 data related to diabetic patients. Finally, the performance of the existing Naïve Bayes algorithm was compared with the proposed algorithm. The results of the comparative study showed that the improvement in the accuracy has been made apparent for the proposed algorithm.

Data Analysis & Statistics Medical, Healthcare, and Life Sciences Health Information Systems