A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population
IntroductionAn easily accessible and cost-free machine learning model based on prior probabilities of vascular aging enables an application to pinpoint high-risk populations before physical checks and optimize healthcare investment.MethodsA dataset containing questionnaire responses and physical mea...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-03-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1365479/full |
| _version_ | 1827313792232456192 |
|---|---|
| author | Rujia Miao Qian Dong Xuelian Liu Yingying Chen Jiangang Wang Jianwen Chen |
| author_facet | Rujia Miao Qian Dong Xuelian Liu Yingying Chen Jiangang Wang Jianwen Chen |
| author_sort | Rujia Miao |
| collection | DOAJ |
| description | IntroductionAn easily accessible and cost-free machine learning model based on prior probabilities of vascular aging enables an application to pinpoint high-risk populations before physical checks and optimize healthcare investment.MethodsA dataset containing questionnaire responses and physical measurement parameters from 77,134 adults was extracted from the electronic records of the Health Management Center at the Third Xiangya Hospital. The least absolute shrinkage and selection operator and recursive feature elimination-Lightweight Gradient Elevator were employed to select features from a pool of potential covariates. The participants were randomly divided into training (70%) and test cohorts (30%). Four machine learning algorithms were applied to build the screening models for elevated arterial stiffness (EAS), and the performance of models was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.ResultsFourteen easily accessible features were selected to construct the model, including “systolic blood pressure” (SBP), “age,” “waist circumference,” “history of hypertension,” “sex,” “exercise,” “awareness of normal blood pressure,” “eat fruit,” “work intensity,” “drink milk,” “eat bean products,” “smoking,” “alcohol consumption,” and “Irritableness.” The extreme gradient boosting (XGBoost) model outperformed the other three models, achieving AUC values of 0.8722 and 0.8710 in the training and test sets, respectively. The most important five features are SBP, age, waist, history of hypertension, and sex.ConclusionThe XGBoost model ideally assesses the prior probability of the current EAS in the general population. The integration of the model into primary care facilities has the potential to lower medical expenses and enhance the management of arterial aging. |
| first_indexed | 2024-04-24T22:22:19Z |
| format | Article |
| id | doaj.art-3aa1e622538d4208aa61e79c6f9022f1 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-24T22:22:19Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-3aa1e622538d4208aa61e79c6f9022f12024-03-20T05:17:13ZengFrontiers Media S.A.Frontiers in Public Health2296-25652024-03-011210.3389/fpubh.2024.13654791365479A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese populationRujia Miao0Qian Dong1Xuelian Liu2Yingying Chen3Jiangang Wang4Jianwen Chen5Health Management Medicine Center, The Third Xiangya Hospital, Central South University, Changsha, ChinaSchool of Science, Hunan University of Technology and Business, Changsha, ChinaHealth Management Medicine Center, The Third Xiangya Hospital, Central South University, Changsha, ChinaHealth Management Medicine Center, The Third Xiangya Hospital, Central South University, Changsha, ChinaHealth Management Medicine Center, The Third Xiangya Hospital, Central South University, Changsha, ChinaSchool of Science, Hunan University of Technology and Business, Changsha, ChinaIntroductionAn easily accessible and cost-free machine learning model based on prior probabilities of vascular aging enables an application to pinpoint high-risk populations before physical checks and optimize healthcare investment.MethodsA dataset containing questionnaire responses and physical measurement parameters from 77,134 adults was extracted from the electronic records of the Health Management Center at the Third Xiangya Hospital. The least absolute shrinkage and selection operator and recursive feature elimination-Lightweight Gradient Elevator were employed to select features from a pool of potential covariates. The participants were randomly divided into training (70%) and test cohorts (30%). Four machine learning algorithms were applied to build the screening models for elevated arterial stiffness (EAS), and the performance of models was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.ResultsFourteen easily accessible features were selected to construct the model, including “systolic blood pressure” (SBP), “age,” “waist circumference,” “history of hypertension,” “sex,” “exercise,” “awareness of normal blood pressure,” “eat fruit,” “work intensity,” “drink milk,” “eat bean products,” “smoking,” “alcohol consumption,” and “Irritableness.” The extreme gradient boosting (XGBoost) model outperformed the other three models, achieving AUC values of 0.8722 and 0.8710 in the training and test sets, respectively. The most important five features are SBP, age, waist, history of hypertension, and sex.ConclusionThe XGBoost model ideally assesses the prior probability of the current EAS in the general population. The integration of the model into primary care facilities has the potential to lower medical expenses and enhance the management of arterial aging.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1365479/fullmachine learningXGBoostarterial stiffnessphysical examinationquestionnairefeature |
| spellingShingle | Rujia Miao Qian Dong Xuelian Liu Yingying Chen Jiangang Wang Jianwen Chen A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population Frontiers in Public Health machine learning XGBoost arterial stiffness physical examination questionnaire feature |
| title | A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population |
| title_full | A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population |
| title_fullStr | A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population |
| title_full_unstemmed | A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population |
| title_short | A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population |
| title_sort | cost effective machine learning driven approach for screening arterial functional aging in a large scale chinese population |
| topic | machine learning XGBoost arterial stiffness physical examination questionnaire feature |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1365479/full |
| work_keys_str_mv | AT rujiamiao acosteffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation AT qiandong acosteffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation AT xuelianliu acosteffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation AT yingyingchen acosteffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation AT jiangangwang acosteffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation AT jianwenchen acosteffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation AT rujiamiao costeffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation AT qiandong costeffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation AT xuelianliu costeffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation AT yingyingchen costeffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation AT jiangangwang costeffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation AT jianwenchen costeffectivemachinelearningdrivenapproachforscreeningarterialfunctionalaginginalargescalechinesepopulation |