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• W3156572054 endingPage "107423" @default.
• W3156572054 startingPage "107423" @default.
• W3156572054 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." @default.
• W3156572054 created "2021-04-26" @default.
• W3156572054 creator A5003692035 @default.
• W3156572054 creator A5068968196 @default.
• W3156572054 date "2021-09-01" @default.
• W3156572054 modified "2023-09-27" @default.
• W3156572054 title "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" @default.
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• W3156572054 doi "https://doi.org/10.1016/j.asoc.2021.107423" @default.
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