Machine learning based models for cardiovascular risk prediction

Nitten S. Rajliwall, Rachel Davey, Girija Chetty

Research output: A Conference proceeding or a Chapter in BookConference contribution

1 Citation (Scopus)

Abstract

Discovering the initial signs of the illness with geospatial information will facilitate and encourage the fact-based policies from health prospective and appropriate management approaches to be developed for diseases. During the paper, we tend to propose a machine learning primarily based prognostic modelling framework, which may run in static/low speed, massive information from electronic health records, furthermore as extreme velocity, streaming massive information settings captured from wearables, like fitness bands and biosensor watches. During our paper, we describe a scalable algorithm called Neuron network, which is used to achieve highly accurate results in fuzzy data. We have presented the outcomes of the proposed framework implementation for static and low-velocity/volume settings from the EHR & clinical DBs, with the experimental authentication of the planned framework, for 2 openly accessible CVD data sets which are 'NHANES' dataset, and the 'Framingham Heart Study' dataset), shown promising outcomes, in terms of performance of different modelling algorithms for the disease status prediction.

Original languageEnglish
Title of host publicationProceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018)
EditorsPhil Kyu Rhee, Daniel Howard, Rezaul Bashar
Place of PublicationNJ, United States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages142-148
Number of pages7
ISBN (Electronic)9781728104041
ISBN (Print)9781728104058
DOIs
Publication statusPublished - 2018
EventInternational Conference on Machine Learning and Data Engineering : iCMLDE 2018 - Western Sydney University, Sydney, Australia
Duration: 3 Dec 20187 Dec 2018
http://www.icmlde.net.au/Home.aspx

Conference

ConferenceInternational Conference on Machine Learning and Data Engineering
CountryAustralia
CitySydney
Period3/12/187/12/18
Internet address

Fingerprint

Cardiovascular Models
Learning systems
Health
Watches
Biosensors
Authentication
Neurons
Chemical vapor deposition
Nutrition Surveys
Electronic Health Records
Biosensing Techniques
Health Policy
Machine Learning
Datasets

Cite this

Rajliwall, N. S., Davey, R., & Chetty, G. (2018). Machine learning based models for cardiovascular risk prediction. In P. K. Rhee, D. Howard, & R. Bashar (Eds.), Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018) (pp. 142-148). NJ, United States: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icmlde.2018.00034
Rajliwall, Nitten S. ; Davey, Rachel ; Chetty, Girija. / Machine learning based models for cardiovascular risk prediction. Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018). editor / Phil Kyu Rhee ; Daniel Howard ; Rezaul Bashar. NJ, United States : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 142-148
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Rajliwall, NS, Davey, R & Chetty, G 2018, Machine learning based models for cardiovascular risk prediction. in PK Rhee, D Howard & R Bashar (eds), Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018). IEEE, Institute of Electrical and Electronics Engineers, NJ, United States, pp. 142-148, International Conference on Machine Learning and Data Engineering , Sydney, Australia, 3/12/18. https://doi.org/10.1109/icmlde.2018.00034

Machine learning based models for cardiovascular risk prediction. / Rajliwall, Nitten S.; Davey, Rachel; Chetty, Girija.

Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018). ed. / Phil Kyu Rhee; Daniel Howard; Rezaul Bashar. NJ, United States : IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 142-148.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Rajliwall NS, Davey R, Chetty G. Machine learning based models for cardiovascular risk prediction. In Rhee PK, Howard D, Bashar R, editors, Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018). NJ, United States: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 142-148 https://doi.org/10.1109/icmlde.2018.00034