An integrated multi-node Hadoop framework to predict high-risk factors of Diabetes Mellitus using a Multilevel MapReduce based Fuzzy Classifier (MMR-FC) and Modified DBSCAN algorithm

Abstract

In the era of data deluge, the world is experiencing an intensive growth of Big data with complex structures. While processing of these data is a complex and labor-intensive process, a proper analysis of Big data leads to greater knowledge extraction. In this paper, Big data is used to predict high-risk factors of Diabetes Mellitus using a new integrated framework with four Hadoop clusters, which are developed to classify the data based on Multi-level MapReduce Fuzzy Classifier (MMR-FC) and MapReduce-Modified Density-Based Spatial Clustering of Applications with Noise (MR-MDBSCAN) algorithm. Big data concerning people’s food habits, physical activity are extracted from social media using the API’s provided. The MMR-FC takes place at three levels of index (Glycemic Index, Physical activity Index, Sleeping Pattern) values. The fuzzy rules are generated by the MMR-FC algorithm to predict the risk of Diabetes Mellitus using the data extracted. The result from MMR-FC is used as an input to the semantic location prediction framework to predict the high-risk zones of Diabetes Mellitus using the MR-MDBSCAN algorithm. The analysis shows that more than 55% of people are in a high-risk group with positive sentiments on the data extracted. More than 70% of food with a high Glycemic Index is usually consumed during Night and Early Evenings, which reveals that people consume food that has a high Glycemic Index during their sedentary slot and have irregular sleep practices. Around 70% of the unhealthiest dietary patterns are retrieved from urban hotspots such as Delhi, Cochin, Kolkata, and Chennai. From the results, it is evident that 55% of younger generations, users of social networking sites having high possibilities of Type II Diabetes Mellitus at large.