Big Data Processing System for Diabetes Prediction using Machine Learning Technique

Abstract

Diabetes is one of the threatening diseases to the entire mankind, though it is not fatal. Irrespective of the presence of several existing approaches for diabetes prediction, big data based diabetes prediction is quite rare. The applicability of the proposed work is wider because, medical records from different sources are extracted and the necessary attributes meant for predicting diabetes alone are processed. The goal of this work is attained by different phases such as data collection, preprocessing, attribute selection and prediction. The diabetes prediction is carried out by Extreme Learning Machine (ELM) classifier. The performance of the proposed approach is analysed by varying the classifiers and the existing approaches in terms of disease prediction accuracy, precision, recall and time consumption. From the experimental results, the efficiency of the work is proven.

An Efficient Exploration on Big Data Analysis in Adolescent Diabetic Prediction with Deep Learning Techniques

“…Before running the DL, they proposed utilising PCA to extract relevant attributes. On a manually gathered dataset from a regional Australian hospital, work [25] used three distinct DL approaches. The dataset consists of 12,000 samples (patients) with a male-tofemale ratio of 55.5 percent.…”