Prediction of Cardiovascular Risk Using Extreme Learning Machine-Tree Classifier on Apache Spark Cluster

(E-pub Ahead of Print)

Author(s): Jaya Lakshmi A, Venkatramaphanikumar S*, Venkata Krishna Kishore Kolli

Journal Name: Recent Advances in Computer Science and Communications
Formerly Recent Patents on Computer Science

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Introduction: In the recent scenario, machine learning is considered a prevailing area in the diverse fields of science and technology include image processing, automobiles, banking, finance, etc. The availability of data and adverse improvements over machine learning techniques have become more feasible to understand and to work on various channels of real-time health analytics.

Method: In this paper, a health status prediction system is proposed to detect cardiovascular diseases through patients’ tweets. Further, analytics will be carried on a distributed Apache Spark framework to reduce the time taken for both training and testing when compared with regular standalone machines.

Results: Performance of the proposed framework with Extreme Learning Machine (ELM) - Tree classifier is evaluated on two different corpora, and which outperforms other classifiers such as Decision Trees, Naïve Bayes, and Linear SVC, DNN, etc. in both accuracy and time.

Discussion: Social media streaming data is considered as one of the major sources for data in the proposed system. Based on the model, the attributes of the incoming user tweets are analyzed, and accordingly cardiovascular risk is predicted. Further, current health status is tweeted back as a reply to the respective user along with a copy to the family and caretakers.

Conclusion: This proposed work provides the development of the alert-based system for heart status prediction by adding some additional features impacting the accuracy besides reducing the response time by using Big data Apache Spark Distributed Framework.

Keywords: Machine learning, social media, streaming data, health status, prediction system, apache spark.

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/2666255813999200904163404
Price: $95

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