Cardiac disease has become one of the major causes of death in recent years. One important risk factor of an individual having cardiac disease is hypercholesterolemia, which also contributes to other diseases, for example, cerebrovascular disease, peripheral arterial disease, and coronary heart disease. However, most cases of cardiac disease were detected late. To detect hypercholesterolemia, we built a few machine-learning models from 336 individuals consisting of several factors, for instance, gender, marital status, alcohol consumption, hypertension, and age, among others. Furthermore, we analyzed the top five factors contributing to hypercholesterolemia. Before building the models, we preprocessed all data. We compared all machine learning models using the cross-validation method. The best model showed a high accuracy score of 0.89 and an AUC score of 0.94.
Skip Nav Destination
,
,
,
,
Article navigation
24 May 2024
ELECTRONIC PHYSICS INFORMATICS INTERNATIONAL CONFERENCE (EPIIC) 2023
25 August 2023
Tangerang, Indonesia
Research Article|
May 24 2024
The prediction of hypercholesterolemia as a risk factor of cardiac disease using machine learning models Available to Purchase
Mega Bagus Herlambang;
Mega Bagus Herlambang
a)
1
Institut Teknologi Indonesia, Industrial Engineering Department
, South Tangerang, Banten, Indonesia
Search for other works by this author on:
Linda Theresia;
Linda Theresia
b)
1
Institut Teknologi Indonesia, Industrial Engineering Department
, South Tangerang, Banten, Indonesia
Search for other works by this author on:
Ni Made Sudri;
Ni Made Sudri
c)
1
Institut Teknologi Indonesia, Industrial Engineering Department
, South Tangerang, Banten, Indonesia
Search for other works by this author on:
Gadih Ranti;
Gadih Ranti
d)
1
Institut Teknologi Indonesia, Industrial Engineering Department
, South Tangerang, Banten, Indonesia
Search for other works by this author on:
Yenny Widianty
Yenny Widianty
e)
2
Institut Teknologi Indonesia, Engineer Profession Department
, South Tangerang, Banten, Indonesia
Search for other works by this author on:
Mega Bagus Herlambang
1,a)
Linda Theresia
1,b)
Ni Made Sudri
1,c)
Gadih Ranti
1,d)
Yenny Widianty
2,e)
1
Institut Teknologi Indonesia, Industrial Engineering Department
, South Tangerang, Banten, Indonesia
2
Institut Teknologi Indonesia, Engineer Profession Department
, South Tangerang, Banten, Indonesia
AIP Conf. Proc. 3116, 030006 (2024)
Citation
Mega Bagus Herlambang, Linda Theresia, Ni Made Sudri, Gadih Ranti, Yenny Widianty; The prediction of hypercholesterolemia as a risk factor of cardiac disease using machine learning models. AIP Conf. Proc. 24 May 2024; 3116 (1): 030006. https://doi.org/10.1063/5.0210219
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
41
Views
Citing articles via
Effect of coupling agent type on the self-cleaning and anti-reflective behaviour of advance nanocoating for PV panels application
Taha Tareq Mohammed, Hadia Kadhim Judran, et al.
Design of a 100 MW solar power plant on wetland in Bangladesh
Apu Kowsar, Sumon Chandra Debnath, et al.
With synthetic data towards part recognition generalized beyond the training instances
Paul Koch, Marian Schlüter, et al.
Related Content
Docking study of compounds in Passiflora edulis, Syzygium cumini, and Averrhoa carambola as cholesterol esterase inhibitor
AIP Conf. Proc. (February 2024)
Inhibitory activity of Uncaria Gambir Roxb extract, ethyl acetate fraction, and catechin isolate on lipase
AIP Conf. Proc. (January 2023)
The effect of incubation time variations on the enrichment of linoleic acid in rice bran oil by solid fermentation method using Aspergillus terreus
AIP Conf. Proc. (September 2021)
Fermentation of pitaya (Hylocereus polyrhizus) juice by L. acidophilus in metabolism of sugars for cholesterol removal
AIP Conf. Proc. (October 2018)
Role of Ganoderma lucidum-derived peptide polysaccharides as antioxidants in diabetic patients with metabolic syndrome
AIP Conf. Proc. (January 2025)