Improving Healthcare Management: Diabetes Prediction via Extreme Learning Machine

Abstract

Recently, the health sector is widely adopting artificial intelligence models such as machine learning 
(ML), deep learning for data analysis, disease prediction, and disease classification. However, the 
conventional models failed to analyze the data.  Therefore, this work is focused on analysis of 
diabetes prediction using extreme learning machine (DP-ELM) model. Initially, Pima Indian diabetes 
is considered, which is pre-processed for missing data symbols identification. Then, the statistical 
features from pre-processed dataset are extracted using principal component analysis (PCA). Then, 
ELM model is trained with the PCA features and forms the trained feature dataset.