An Efficient Method of Classification the Gestational Diabetes Using ID3 Classifier

Abstract

Diabetes is a disease that produces high blood sugar levels and is one of the most deadly and long-term illnesses. Artificial intelligence plays an essential role in this sector since High-performance and accuracy of Artificial intelligence algorithms can be used to reduce classification errors by enhancing classification accuracy in detecting and classifying gestational diabetes. The Iterative Dichotomiser3 (ID3) algorithm, which is utilized to identify gestational diabetes, was one of the most significant algorithms employed in this study, and it produced findings with high performance and accuracy of 94 percent. The Pima Indians Diabetes dataset uses machine learning to address this issue in the community. The results of the proposed method were examined and confirmed for accuracy using data from the University of California, Irvine (UCI) database; this strategy can be used to categorize diabetes in the health sector.