| Issue |
MATEC Web Conf.
Volume 125, 2017
21st International Conference on Circuits, Systems, Communications and Computers (CSCC 2017)
|
|
|---|---|---|
| Article Number | 02071 | |
| Number of page(s) | 5 | |
| Section | Systems | |
| DOI | https://doi.org/10.1051/matecconf/201712502071 | |
| Published online | 04 October 2017 | |
Online Adaptive Coronary Heart Disease Risk Prediction Model
1 Advanced Diagnostics and Progressive Human Care Research Group, IJN-UTM Cardiovascular Engineering Centre, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Malaysia.
2 National Heart Institute, Kuala Lumpur, Malaysia
3 National Cardiovascular Centre, Jakarta, Indonesia
* Corresponding author: eko@utm.my
Coronary Heart Disease (CHD) is the leading causes of death worldwide. Life style changing is one of the important methods to delay the incidence of CHD. The awareness of life style changing is however still low. In order to improve awareness of life style changing, some CHD risk prediction models have been introduced. The existing models however either not well structured, not completed, static or offline. This paper introduces a new online CHD risk prediction model. The model is structured according to three risk factor groups including molecular structure, body system vital sign and bioenergy symphony. The model had also been compared with 5 existing models. Comparison results show that the model has better structure, adaptability and accessibility. Validation test using 120 subjects shows that the model prediction accuracy is 96.2%. This shows that the model is suitable to be used widely for CHD risk prediction both healthy and risk subjects as a preventive method in getting CHD in the earlier age.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.