Hybrid Online Model for Predicting Diabetes Mellitus

Abstract

Modern healthcare systems have become smart by synergizing the potentials of wireless sensors, the medical Internet of things, and big data science to provide better patient care while decreasing medical expenses. Large healthcare organizations generate and accumulate an incredible volume of data continuously. The already daunting volume of medical information has a massive amount of diagnostic features and logged details of patients for certain diseases such as diabetes. Diabetes mellitus has emerged as along-haul fatal disease across the globe and particularly in developing countries. Exact and early diagnosis of diabetes from big medical data is vital for the deterrence of disease and the selection of proper therapy. Traditional machine learning-based diagnosis systems have been initially established as offline (non-incremental) approaches that are trained with a pre-defined database before they can be applied to handle prediction problems. The major objective of the proposed work is to predict and classify diabetes mellitus by implementing a Hybrid Online Model for Early Detection of diabetes disease (HOMED) using machine learning algorithms. Our proposed online (incremental) diabetes diagnosis system exploits (i) an Adaptive Principal Component Analysis (APCA) technique for missing value imputation, data clustering, and feature selection; and (ii) an enhanced incremental support vector machine (ISVM) for classification. The efficiency of HOMED is estimated on different performance metrics such as accuracy, precision, specificity, sensitivity, positive predictive value, and negative predictive value. Experimental results on Pima Indian diabetes dataset (768 samples: 500 non diabetic and 268 diabetic patients) reveal that HOMED considerably increases the classification accuracy and decreases computational complexity with respect to the offline models. The proposed system can assist healthcare professionals as a decision support system.