Diagnosis of Adolescent diabetes based cardiac risk Prediction using Map Reduce Algorithm with Hadoop clustering and using Hybrid Architecture of Neural Network

Abstract

Diabetes Mellitus (DM) is a prevalent chronic condition that can lead to serious health consequences and even death. The early detection of diabetes is critical, and significant complexity needs to occur countered. Many research studies on diabetes diagnosis have to obtain, the majority of which do base on a single data set, the Pima Indian diabetes data set. As a result, early detection and treatment are essential for illness prevention. Machine Learning (ML) approaches are self-handled to make more accurate predictions and improve performance. Hadoop/Map Reduce system was employed and a predictive analysis method to anticipate the forms of diabetes that are prevalent, the difficultiesrelated with it and type of therapy that will deliver. This approach, according to the report, provides significant way to cure and care for patients with superior outcomes such as affordability as well as availability. Hadoop Distributed File System (HDFS) is used to store a large set of data and Map Reduce programming model is used to analyze the massive amount of patient data sets in parallel programming method. Using this analysis it is possible to identify patients likely to suffer from diabetic risk and diagnose the patient at the earliest. Once the diabetes range is detected, for those patients with an abnormal diabetic range from their pre-historic medical data will obtain analysis for predicting the risk of cardiac attack. The data has been collected as images and classified using the hybrid architecture oftheConvolutionalneural network (CNN) that comprises VGG-19 and inception V3 algorithm. The experiment to detect diabetes at an early stage and performance of these methodsis validated using measures viz., Accuracy of 97.8%, Precision 96.9%, Recall 86.9%, and F-Measure 76.9%.