Online Adaptive Coronary Heart Disease Risk Prediction Model

Jostinah Lam, Eko Supriyanto, Faris Yahya, Muhammad Haikal Satria, Suhaini Kadiman, Aizai Azan, Amiliana Mardiani Soesanto

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number02071
JournalMATEC Web of Conferences
Volume125
DOIs
Publication statusPublished - 4 Oct 2017
Event21st International Conference on Circuits, Systems, Communications and Computers, CSCC 2017 - Heraklion, Crete, Greece
Duration: 14 Jul 201717 Jul 2017

Fingerprint Dive into the research topics of 'Online Adaptive Coronary Heart Disease Risk Prediction Model'. Together they form a unique fingerprint.

  • Cite this