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Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has hig...

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Autores principales: Dritsas, Elias, Trigka, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322993/
https://www.ncbi.nlm.nih.gov/pubmed/35891045
http://dx.doi.org/10.3390/s22145365
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author Dritsas, Elias
Trigka, Maria
author_facet Dritsas, Elias
Trigka, Maria
author_sort Dritsas, Elias
collection PubMed
description Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has high cholesterol (hypercholesterolemia), the blood vessels are blocked by fats, and thus, circulation through the arteries becomes difficult. The heart does not receive the oxygen it needs, and the risk of heart attack increases. Nowadays, machine learning (ML) has gained special interest from physicians, medical centers and healthcare providers due to its key capabilities in health-related issues, such as risk prediction, prognosis, treatment and management of various conditions. In this article, a supervised ML methodology is outlined whose main objective is to create risk prediction tools with high efficiency for hypercholesterolemia occurrence. Specifically, a data understanding analysis is conducted to explore the features association and importance to hypercholesterolemia. These factors are utilized to train and test several ML models to find the most efficient for our purpose. For the evaluation of the ML models, precision, recall, accuracy, F-measure, and AUC metrics have been taken into consideration. The derived results highlighted Soft Voting with Rotation and Random Forest trees as base models, which achieved better performance in comparison to the other models with an AUC of 94.5%, precision of 92%, recall of 91.8%, F-measure of 91.7% and an accuracy equal to 91.75%.
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spelling pubmed-93229932022-07-27 Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction Dritsas, Elias Trigka, Maria Sensors (Basel) Article Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has high cholesterol (hypercholesterolemia), the blood vessels are blocked by fats, and thus, circulation through the arteries becomes difficult. The heart does not receive the oxygen it needs, and the risk of heart attack increases. Nowadays, machine learning (ML) has gained special interest from physicians, medical centers and healthcare providers due to its key capabilities in health-related issues, such as risk prediction, prognosis, treatment and management of various conditions. In this article, a supervised ML methodology is outlined whose main objective is to create risk prediction tools with high efficiency for hypercholesterolemia occurrence. Specifically, a data understanding analysis is conducted to explore the features association and importance to hypercholesterolemia. These factors are utilized to train and test several ML models to find the most efficient for our purpose. For the evaluation of the ML models, precision, recall, accuracy, F-measure, and AUC metrics have been taken into consideration. The derived results highlighted Soft Voting with Rotation and Random Forest trees as base models, which achieved better performance in comparison to the other models with an AUC of 94.5%, precision of 92%, recall of 91.8%, F-measure of 91.7% and an accuracy equal to 91.75%. MDPI 2022-07-18 /pmc/articles/PMC9322993/ /pubmed/35891045 http://dx.doi.org/10.3390/s22145365 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dritsas, Elias
Trigka, Maria
Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction
title Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction
title_full Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction
title_fullStr Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction
title_full_unstemmed Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction
title_short Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction
title_sort machine learning methods for hypercholesterolemia long-term risk prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322993/
https://www.ncbi.nlm.nih.gov/pubmed/35891045
http://dx.doi.org/10.3390/s22145365
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