AIMC Topic: Heart Disease Risk Factors
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Key factors in predictive analysis of cardiovascular risks in public health.
Scientific reports
Jul 2, 2025
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...
Enhanced cardiovascular risk prediction in the Western Pacific: A machine learning approach tailored to the Malaysian population.
PloS one
Jun 17, 2025
BACKGROUND: Cardiovascular disease (CVD) is a significant public health challenge in the Western Pacific region, including Malaysia.
Optimized machine learning mechanism for big data healthcare system to predict disease risk factor.
Scientific reports
Apr 24, 2025
Heart disease is becoming more and more common in modern society because of factors like stress, inadequate diets, etc. Early identification of heart disease risk factors is essential as it allows for treatment plans that may reduce the risk of sever...
Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors.
Cardiovascular diabetology
Apr 2, 2025
BACKGROUND: Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality globally. Traditional risk models, primarily based on established risk factors, often lack the precision needed to accurately predict new-onset major advers...
Machine learning analysis of cardiovascular risk factors and their associations with hearing loss.
Scientific reports
Mar 22, 2025
Hearing loss poses immense burden worldwide and early detection is crucial. The accurate models identify high-risk groups, enabling timely intervention to improve quality of life. The subtle changes in hearing often go unnoticed, presenting a challen...
Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review.
Journal of the American Heart Association
Mar 13, 2025
Electronic health records (EHR) have revolutionized cardiovascular disease (CVD) research by enabling comprehensive, large-scale, and dynamic data collection. Integrating EHR data with advanced analytical methods, including artificial intelligence (A...
Characterizing patients at higher cardiovascular risk for prescribed stimulants: Learning from health records data with predictive analytics and data mining techniques.
Computers in biology and medicine
Feb 20, 2025
OBJECTIVE: Given the significantly increased number of individuals prescribed stimulants in the past decade, there has been growing concern regarding the risk of cardiovascular events among adults on stimulant therapy. We aimed to quantify the added ...
Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.
Cardiovascular diabetology
Feb 15, 2025
BACKGROUND: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs), however the predictive performance of traditional risk prediction methods are limited.
Predicting cardiovascular outcomes in Chinese patients with type 2 diabetes by combining risk factor trajectories and machine learning algorithm: a cohort study.
Cardiovascular diabetology
Feb 7, 2025
BACKGROUND: Cardiovascular complications are major concerns for Chinese patients with type 2 diabetes. Accurately predicting these risks remains challenging due to limitations in traditional risk models. We aimed to develop a dynamic prediction model...
Comparison of cardiovascular risk prediction models developed using machine learning based on data from a Sri Lankan cohort with World Health Organization risk charts for predicting cardiovascular risk among Sri Lankans: a cohort study.
BMJ open
Jan 15, 2025
INTRODUCTION: Models derived from non-Sri Lankan cohorts are used for cardiovascular (CV) risk stratification of Sri Lankans.
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