Key factors in predictive analysis of cardiovascular risks in public health
Abstract This research emphasizes the role of analytics in evaluating the risk of disease (CVD) focusing on thorough data preparation and feature engineering for accurate predictions. We studied machine learning (ML) and learning (DL) models, such as Logistic Regression (LR) Random Forest (RF) Gradi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-07874-x |
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| author | Ghazi I. Al Jowf Manjur Kolhar |
| author_facet | Ghazi I. Al Jowf Manjur Kolhar |
| author_sort | Ghazi I. Al Jowf |
| collection | DOAJ |
| description | Abstract This research emphasizes the role of analytics in evaluating the risk of disease (CVD) focusing on thorough data preparation and feature engineering for accurate predictions. We studied machine learning (ML) and learning (DL) models, such as Logistic Regression (LR) Random Forest (RF) Gradient Boosting Machines (GBM) and Multilayer Perceptron (MLP). Each model’s performance was assessed using metrics like accuracy, precision, recall, F1 score and ROC AUC to determine their reliability and practical relevance. Our analysis shows the strengths of each model category. Conventional ML models like Random Forest and Gradient Boosting Machines were effective in identifying patients at risk achieving up to 74% accuracy and 72% recall. On the hand, deep learning models like Multilayer Perceptron excelled in handling data with an impressive ROC AUC score of approximately 80%. Despite the need for resources and extensive data preprocessing these models are highly skilled at pinpointing crucial risk factors, crucial, for long term CVD management. |
| format | Article |
| id | doaj-art-c86a76a0bddb485e8a1bcb8ffa35b3b2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c86a76a0bddb485e8a1bcb8ffa35b3b22025-08-20T04:01:25ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-07874-xKey factors in predictive analysis of cardiovascular risks in public healthGhazi I. Al Jowf0Manjur Kolhar1Department Public Health, College of Applied Medical Sciences, King Faisal UniversityDepartment Health Information Management and Technology, College of Applied Medical Sciences, King Faisal UniversityAbstract This research emphasizes the role of analytics in evaluating the risk of disease (CVD) focusing on thorough data preparation and feature engineering for accurate predictions. We studied machine learning (ML) and learning (DL) models, such as Logistic Regression (LR) Random Forest (RF) Gradient Boosting Machines (GBM) and Multilayer Perceptron (MLP). Each model’s performance was assessed using metrics like accuracy, precision, recall, F1 score and ROC AUC to determine their reliability and practical relevance. Our analysis shows the strengths of each model category. Conventional ML models like Random Forest and Gradient Boosting Machines were effective in identifying patients at risk achieving up to 74% accuracy and 72% recall. On the hand, deep learning models like Multilayer Perceptron excelled in handling data with an impressive ROC AUC score of approximately 80%. Despite the need for resources and extensive data preprocessing these models are highly skilled at pinpointing crucial risk factors, crucial, for long term CVD management.https://doi.org/10.1038/s41598-025-07874-xCardiovascularMachine learningDeep learningRegressionRandom forestGradient boosting machines |
| spellingShingle | Ghazi I. Al Jowf Manjur Kolhar Key factors in predictive analysis of cardiovascular risks in public health Scientific Reports Cardiovascular Machine learning Deep learning Regression Random forest Gradient boosting machines |
| title | Key factors in predictive analysis of cardiovascular risks in public health |
| title_full | Key factors in predictive analysis of cardiovascular risks in public health |
| title_fullStr | Key factors in predictive analysis of cardiovascular risks in public health |
| title_full_unstemmed | Key factors in predictive analysis of cardiovascular risks in public health |
| title_short | Key factors in predictive analysis of cardiovascular risks in public health |
| title_sort | key factors in predictive analysis of cardiovascular risks in public health |
| topic | Cardiovascular Machine learning Deep learning Regression Random forest Gradient boosting machines |
| url | https://doi.org/10.1038/s41598-025-07874-x |
| work_keys_str_mv | AT ghaziialjowf keyfactorsinpredictiveanalysisofcardiovascularrisksinpublichealth AT manjurkolhar keyfactorsinpredictiveanalysisofcardiovascularrisksinpublichealth |