Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning

Early risk factor detection is essential for managing and treating cardiovascular disease (CVD), a global health issue. Studies have shown that waist circumference (WC) and waist hip ratio (WHR) are better at identifying CVD than BMI. The study uses Random Forest (RF) machine learning to identify ch...

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Main Authors: Zeynep Kucukakcali, Ipek Balikci Cicek
Format: Article
Language:English
Published: Society of Turaz Bilim 2024-04-01
Series:Medicine Science
Subjects:
Online Access:https://www.medicinescience.org/?mno=215234
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author Zeynep Kucukakcali
Ipek Balikci Cicek
author_facet Zeynep Kucukakcali
Ipek Balikci Cicek
author_sort Zeynep Kucukakcali
collection DOAJ
description Early risk factor detection is essential for managing and treating cardiovascular disease (CVD), a global health issue. Studies have shown that waist circumference (WC) and waist hip ratio (WHR) are better at identifying CVD than BMI. The study uses Random Forest (RF) machine learning to identify characteristics that affect WHR, an indication of CVD. Isfahan Cardiovascular Research Centre in Iran provided the dataset, which includes sex, family history, diabetes, WHR, smoking, systolic blood pressure, and total cholesterol. Statistical analyses employed Yates' correction and Pearson chi-squared tests. Modeling with RF yielded accuracy, balanced accuracy, sensitivity, specificity, PPV, NPV, and F1 score from performance metrics. Finally, variable significance values determined the dependent variable's most relevant variables. WHR and other variables are statistically significantly correlated. Random Forest machine learning predicts high WHR with high accuracy, sensitivity and specificity. The most important variables of the prediction model are female sex, smoking status and blood pressure ranges. In conclusion, the global burden of CVD and the necessity of early diagnosis are underlined. The role of WHR along with BMI and waist circumference in the assessment of cardiovascular risk is emphasised. The study concludes that the machine learning model can effectively predict high WHR, aid CVD risk management and facilitate personalised treatment plans. The results contribute to a better understanding of the factors influencing high WHR and can guide healthcare professionals in the comprehensive assessment and management of cardiovascular risks. [Med-Science 2024; 13(4.000): 866-72]
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spelling doaj-art-79d1e16f8de64b82945ef8f913009e152025-02-07T08:49:23ZengSociety of Turaz BilimMedicine Science2147-06342024-04-011348667210.5455/medscience.2024.08.094215234Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learningZeynep Kucukakcali0Ipek Balikci Cicek1Inonu University Inonu UniversityEarly risk factor detection is essential for managing and treating cardiovascular disease (CVD), a global health issue. Studies have shown that waist circumference (WC) and waist hip ratio (WHR) are better at identifying CVD than BMI. The study uses Random Forest (RF) machine learning to identify characteristics that affect WHR, an indication of CVD. Isfahan Cardiovascular Research Centre in Iran provided the dataset, which includes sex, family history, diabetes, WHR, smoking, systolic blood pressure, and total cholesterol. Statistical analyses employed Yates' correction and Pearson chi-squared tests. Modeling with RF yielded accuracy, balanced accuracy, sensitivity, specificity, PPV, NPV, and F1 score from performance metrics. Finally, variable significance values determined the dependent variable's most relevant variables. WHR and other variables are statistically significantly correlated. Random Forest machine learning predicts high WHR with high accuracy, sensitivity and specificity. The most important variables of the prediction model are female sex, smoking status and blood pressure ranges. In conclusion, the global burden of CVD and the necessity of early diagnosis are underlined. The role of WHR along with BMI and waist circumference in the assessment of cardiovascular risk is emphasised. The study concludes that the machine learning model can effectively predict high WHR, aid CVD risk management and facilitate personalised treatment plans. The results contribute to a better understanding of the factors influencing high WHR and can guide healthcare professionals in the comprehensive assessment and management of cardiovascular risks. [Med-Science 2024; 13(4.000): 866-72]https://www.medicinescience.org/?mno=215234cardiovascular diseaserisk factorwaist hip ratiomachine learning
spellingShingle Zeynep Kucukakcali
Ipek Balikci Cicek
Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning
Medicine Science
cardiovascular disease
risk factor
waist hip ratio
machine learning
title Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning
title_full Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning
title_fullStr Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning
title_full_unstemmed Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning
title_short Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning
title_sort prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning
topic cardiovascular disease
risk factor
waist hip ratio
machine learning
url https://www.medicinescience.org/?mno=215234
work_keys_str_mv AT zeynepkucukakcali predictionofcardiovasculardiseasefromfactorsassociatedwithwaisthipratiobymachinelearning
AT ipekbalikcicicek predictionofcardiovasculardiseasefromfactorsassociatedwithwaisthipratiobymachinelearning