相关论文: Heart Disease Prediction System using Associative …

Cardiovascular disease is the primary cause of death globally, necessitating early identification, precise risk classification, and dependable decision-support technologies. The advent of large language models (LLMs) provides new zero-shot…

Background: The concept of combinatorial biomarkers was conceived around 2010: it was noticed that simple biomarkers are often inadequate for recognizing and characterizing complex diseases. Methods: Here we present an algorithmic search…

Background: Selecting feature genes to predict phenotypes is one of the typical tasks in analyzing genomics data. Though many general-purpose algorithms were developed for prediction, dealing with highly correlated genes in the prediction…

Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among cardiac ailments. Early diagnosis is crucial in providing intervention for patients suffering from cardiac arrhythmia. Traditionally, diagnosis is…

ICU mortality scoring systems attempt to predict patient mortality using predictive models with various clinical predictors. Examples of such systems are APACHE, SAPS and MPM. However, most such scoring systems do not actively look for and…

We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment…

Cardiovascular disease (CVD) remains a critical global health concern, demanding reliable and interpretable predictive models for early risk assessment. This study presents a large-scale analysis using the Heart Disease Health Indicators…

Ischemic heart disease (IHD), particularly in its chronic stable form, is a subtle pathology due to its silent behavior before developing in unstable angina, myocardial infarction or sudden cardiac death. Machine learning techniques applied…

From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on genetic variations at the DNA base pair level, called Single-Nucleotide Polymorphisms (SNPs), collected from the Ontario Heart…

Metabolomic data can potentially enable accurate, non-invasive and low-cost prediction of coronary artery disease. Regression-based analytical approaches however might fail to fully account for interactions between metabolites, rely on a…

Many people are currently suffering from heart diseases that can lead to untimely death. The most common heart abnormality is arrhythmia, which is simply irregular beating of the heart. A prediction system for the early intervention and…

Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. Also considering the increase of medical data, much pressure is put on the…

Disease gene prioritization assigns scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. Here, we describe a new algorithm for disease gene prioritization based on…

We intend to create a new risk assessment methodology that combines the best characteristics of both risk score and machine learning models. More specifically, we aim to develop a method that, besides having a good performance, offers a…

Despite extensive research during the last decades, coronary artery disease (CAD) remains the number one cause of death, responsible for near 50% of global mortality. A main reason for this is that CAD has a complex inheritance and etiology…

Premature coronary artery disease (PCAD) refers to the early onset of the disease, usually before the age of 55 for men and 65 for women. Coronary Artery Disease (CAD) develops when coronary arteries, the major blood vessels supplying the…

Cardiac magnetic resonance imaging improves on diagnosis of cardiovascular diseases by providing images at high spatiotemporal resolution. Manual evaluation of these time-series, however, is expensive and prone to biased and…

Rare diseases are very difficult to identify among large number of other possible diagnoses. Better availability of patient data and improvement in machine learning algorithms empower us to tackle this problem computationally. In this…

Cardiovascular diseases are the most common cause of mortality worldwide. Detection of atrial fibrillation (AF) in the asymptomatic stage can help prevent strokes. It also improves clinical decision making through the delivery of suitable…

Aiming at the latest particle swarm optimization algorithm, this paper proposes an improved Transformer model to improve the accuracy of heart disease prediction and provide a new algorithm idea. We first use three mainstream machine…