Abstract
Detection of early signs of the diseases at geospatial level can help in promoting evidence-based health policies and proper disease management strategies to be formulated beforehand. In this paper, we report the performance of XGboosting algorithm as a part of a novel unified predictive modelling framework. The proposed scalable end to end tree boosting system called XGBoost, results in significantly improved performance. The experimental validation of the proposed algorithm, when compared with traditional shallow machine learning techniques, for two publicly available cardiovascular disease datasets (the NHANES dataset, and the Framingham Heart Study CHS dataset), resulted in promising outcomes, in terms of performance of different performance metrics including prediction accuracy and model building time.
Original language | English |
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Title of host publication | Proceedings: 2018 5th Asia-Pacific World Congress on Computer Science and Engineering |
Subtitle of host publication | APWConCSE 2018 |
Editors | A B M Shawkat Ali, Shah Miah, Maheswara Rao Valluri |
Place of Publication | Danvers, USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 246-252 |
Number of pages | 7 |
ISBN (Print) | 9781728113906 |
DOIs | |
Publication status | Published - 10 Dec 2018 |
Event | 5th Asia-Pacific World Congress on Computer Science and Engineering 2018 (APWC on CSE) - Momi Bay, Fiji, Nadi, Fiji Duration: 10 Dec 2018 → 12 Dec 2018 http://ilab-australia.org/apwconcse2018/ |
Publication series
Name | Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018 |
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Conference
Conference | 5th Asia-Pacific World Congress on Computer Science and Engineering 2018 (APWC on CSE) |
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Abbreviated title | (APWC on CSE |
Country/Territory | Fiji |
City | Nadi |
Period | 10/12/18 → 12/12/18 |
Internet address |