Towards personalized cardiometabolic risk prediction: A fusion of exposome and AI
The influence of the exposome on major health conditions like cardiovascular disease (CVD) is widely recognized. However, integrating diverse exposome factors into predictive models for personalized health assessments remains a challenge due to the complexity and variability of environmental exposur...
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| Language: | English |
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Elsevier
2025-01-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024168909 |
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| author | Zeinab Shahbazi Slawomir Nowaczyk |
| author_facet | Zeinab Shahbazi Slawomir Nowaczyk |
| author_sort | Zeinab Shahbazi |
| collection | DOAJ |
| description | The influence of the exposome on major health conditions like cardiovascular disease (CVD) is widely recognized. However, integrating diverse exposome factors into predictive models for personalized health assessments remains a challenge due to the complexity and variability of environmental exposures and lifestyle factors. A machine learning (ML) model designed for predicting CVD risk is introduced in this study, relying on easily accessible exposome factors. This approach is particularly novel as it prioritizes non-clinical, modifiable exposures, making it applicable for broad public health screening and personalized risk assessments. Assessments were conducted using both internal and external validation groups from a multi-center cohort, comprising 3,237 individuals diagnosed with CVD in South Korea within twelve years of their baseline visit, along with an equal number of participants without these conditions as a control group. Examination of 109 exposome variables from participants' baseline visits spanned physical measures, environmental factors, lifestyle choices, mental health events, and early-life factors. For risk prediction, the Random Forest classifier was employed, with performance compared to an integrative ML model using clinical and physical variables. Furthermore, data preprocessing involved normalization and handling of missing values to enhance model accuracy. The model's decision-making process were using an advanced explainability method. Results indicated comparable performance between the exposome-based ML model and the integrative model, achieving AUC of 0.82(+/-)0.01, 0.70(+/-)0.01, and 0.73(+/-)0.01. The study underscores the potential of leveraging exposome data for early intervention strategies. Additionally, exposome factors significant in identifying CVD risk were pinpointed, including daytime naps, completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status. |
| format | Article |
| id | doaj-art-3a680d6189034b97b998cd9868d41bca |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-3a680d6189034b97b998cd9868d41bca2025-01-17T04:49:48ZengElsevierHeliyon2405-84402025-01-01111e40859Towards personalized cardiometabolic risk prediction: A fusion of exposome and AIZeinab Shahbazi0Slawomir Nowaczyk1Corresponding author.; Center for Applied Intelligent Systems Research, Halmstad University, SwedenCenter for Applied Intelligent Systems Research, Halmstad University, SwedenThe influence of the exposome on major health conditions like cardiovascular disease (CVD) is widely recognized. However, integrating diverse exposome factors into predictive models for personalized health assessments remains a challenge due to the complexity and variability of environmental exposures and lifestyle factors. A machine learning (ML) model designed for predicting CVD risk is introduced in this study, relying on easily accessible exposome factors. This approach is particularly novel as it prioritizes non-clinical, modifiable exposures, making it applicable for broad public health screening and personalized risk assessments. Assessments were conducted using both internal and external validation groups from a multi-center cohort, comprising 3,237 individuals diagnosed with CVD in South Korea within twelve years of their baseline visit, along with an equal number of participants without these conditions as a control group. Examination of 109 exposome variables from participants' baseline visits spanned physical measures, environmental factors, lifestyle choices, mental health events, and early-life factors. For risk prediction, the Random Forest classifier was employed, with performance compared to an integrative ML model using clinical and physical variables. Furthermore, data preprocessing involved normalization and handling of missing values to enhance model accuracy. The model's decision-making process were using an advanced explainability method. Results indicated comparable performance between the exposome-based ML model and the integrative model, achieving AUC of 0.82(+/-)0.01, 0.70(+/-)0.01, and 0.73(+/-)0.01. The study underscores the potential of leveraging exposome data for early intervention strategies. Additionally, exposome factors significant in identifying CVD risk were pinpointed, including daytime naps, completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status.http://www.sciencedirect.com/science/article/pii/S2405844024168909Cardiovascular diseaseExposomeClinical recordsArtificial intelligenceMachine learning |
| spellingShingle | Zeinab Shahbazi Slawomir Nowaczyk Towards personalized cardiometabolic risk prediction: A fusion of exposome and AI Heliyon Cardiovascular disease Exposome Clinical records Artificial intelligence Machine learning |
| title | Towards personalized cardiometabolic risk prediction: A fusion of exposome and AI |
| title_full | Towards personalized cardiometabolic risk prediction: A fusion of exposome and AI |
| title_fullStr | Towards personalized cardiometabolic risk prediction: A fusion of exposome and AI |
| title_full_unstemmed | Towards personalized cardiometabolic risk prediction: A fusion of exposome and AI |
| title_short | Towards personalized cardiometabolic risk prediction: A fusion of exposome and AI |
| title_sort | towards personalized cardiometabolic risk prediction a fusion of exposome and ai |
| topic | Cardiovascular disease Exposome Clinical records Artificial intelligence Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024168909 |
| work_keys_str_mv | AT zeinabshahbazi towardspersonalizedcardiometabolicriskpredictionafusionofexposomeandai AT slawomirnowaczyk towardspersonalizedcardiometabolicriskpredictionafusionofexposomeandai |