Implementing Big Data analytics to predict Systemic Lupus Erythematosus

Gomathi, S.; Narayani, V.

doi:10.1109/iciiecs.2015.7192893

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…There are few works where ensemble techniques for big data is parallelized with mapreduce [268], yet they are not tried on platforms such as spark that are proved to be more efficient than mapreduce. Predictive analytics on health big data helps in predicting the spread of diseases [8], [9], predicting chances of readmission in hospitals [10], predicting the diseases at an early stage, [11], [12],clinical decision support system to identify the right treatment for the affected patients, hospital management etc [13]. A detailed overview about the use of predictive analytics in health informatics is presented in [14].…”

Section: Ensemble Algorithmsmentioning

confidence: 99%

“…Past data is combined with real time data for prediction. [11] proposes a predictive model to predict Systemic Lupus Erythematosus, a disease that affects multiple organs by integrating structured data like electronic health records, unstructured and semi structured data like imaging and scan tests(MRI, CT, Ultrasound scan, X-ray), complete blood count, urinalysis. [8] develops a spatial data model to predict influenza epidemic in Vellore, India.…”

Section: Data Integrationmentioning

confidence: 99%

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Predictive Analytics On Big Data - An Overview

Nagarajan

Babu

2019

IJCAI

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Big data generated in different domains and industries are voluminous and the velocity at which they are generated is pretty high. While research works carried out continuously to handle big data is at one end, processing it to develop the business insights is a hot topic to work on the other end. Though there are lot of technologies and tools developed to handle big data and extract insights from them, there are lot of challenges and open issues that are yet to be addressed. This paper presents an overview on predictive analytics with big data. The overview throws light on the core predictive models, challenges of these models on big data, research gaps in several domain sectors and using different techniques. This paper categorizes the major technical challenges of predictive analytics on big data under six headings. The paper concludes with the identification of open issues and future directions to work on for researchers in this field.Povzetek: Pregledničlanek opisuje prediktivno analitiko na velikih podatkih. Informatica 43 (2019) 425-459 G. Nagarajan et al.

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