ArticleScientific reports2025
Key factors in predictive analysis of cardiovascular risks in public health.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Harnessing Clinical and Biochemical Data for Personalized Cardiovascular Risk Prediction: a Machine Learning Approach Toward Precision Nutrition.The Journal of nutrition · 2026Observational
- A risk prediction model for gastrointestinal bleeding in coronary heart disease patients undergoing dual antiplatelet therapy after percutaneous coronary intervention.Pakistan journal of medical sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.