TY - JOUR
T1 - Machine learning in coronary heart disease prediction
T2 - Structural equation modelling approach
AU - Rodrigues, Lewlyn L.R.
AU - Shetty, Dasharathraj K.
AU - Naik, Nithesh
AU - Maddodi, Chethana Balakrishna
AU - Rao, Anuradha
AU - Shetty, Ajith Kumar
AU - Bhat, Rama
AU - Hameed, Zeeshan
N1 - Publisher Copyright:
© 2020, © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - This research is an application of machine learning in medical sciences. The purpose of this research was to use machine learning through the simulated data to study the association of age, body mass index, cigarettes smoked per day, alcohol consumed per week, diastolic blood pressure, and systolic blood pressure on hypertension and coronary heart disease. The Structural Equation Modelling using Partial Least Square Method was used for the analysis of data. The results have revealed that except for age, body mass index and systolic blood pressure all the rest of the factors had a significant positive association with hypertension and coronary heart disease. The results can be of use for medical practitioners as well as researchers in machine learning, as it adds to the repository of earlier studies, which have attempted to seek relationships between these variables.
AB - This research is an application of machine learning in medical sciences. The purpose of this research was to use machine learning through the simulated data to study the association of age, body mass index, cigarettes smoked per day, alcohol consumed per week, diastolic blood pressure, and systolic blood pressure on hypertension and coronary heart disease. The Structural Equation Modelling using Partial Least Square Method was used for the analysis of data. The results have revealed that except for age, body mass index and systolic blood pressure all the rest of the factors had a significant positive association with hypertension and coronary heart disease. The results can be of use for medical practitioners as well as researchers in machine learning, as it adds to the repository of earlier studies, which have attempted to seek relationships between these variables.
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U2 - 10.1080/23311916.2020.1723198
DO - 10.1080/23311916.2020.1723198
M3 - Article
AN - SCOPUS:85079422007
SN - 2331-1916
VL - 7
JO - Cogent Engineering
JF - Cogent Engineering
IS - 1
M1 - 1723198
ER -