Related papers: Performance Analysis of Machine Learning Algorithm…
Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide…
The shortage of nephrologists and the growing public health concern over renal failure have spurred the demand for AI systems capable of autonomously detecting kidney abnormalities. Renal failure, marked by a gradual decline in kidney…
With Polycystic Kidney Disease (PKD) potentially leading to fatal complications in patients due to the formation of cysts in kidneys, early detection of PKD is crucial for effective management of the condition. However, the various…
Heart disease is the leading cause of death worldwide. Currently, 33% of cases are misdiagnosed, and approximately half of myocardial infarctions occur in people who are not predicted to be at risk. The use of Artificial Intelligence could…
In today's world, a massive amount of data is available in almost every sector. This data has become an asset as we can use this enormous amount of data to find information. Mainly health care industry contains many data consisting of…
Objective: To improve prediction of Chronic Kidney Disease (CKD) progression to End Stage Renal Disease (ESRD) using machine learning (ML) and deep learning (DL) models applied to an integrated clinical and claims dataset of varying…
Coronary Artery Disease (CAD) is one of the leading causes of death worldwide, and so it is very important to correctly diagnose patients with the disease. For medical diagnosis, machine learning is a useful tool, however features and…
The recent increase in morbidity is primarily due to chronic diseases including Diabetes, Heart disease, Lung cancer, and brain tumours. The results for patients can be improved, and the financial burden on the healthcare system can be…
Background: Hypertensive kidney disease (HKD) patients in intensive care units (ICUs) face high short-term mortality, but tailored risk prediction tools are lacking. Early identification of high-risk individuals is crucial for clinical…
Randomized controlled trials (RCTs) are the standard for evaluating the effectiveness of clinical interventions. To address the limitations of RCTs on real-world populations, we developed a methodology that uses a large observational…
Medical image classification is a vital research area that utilizes advanced computational techniques to improve disease diagnosis and treatment planning. Deep learning models, especially Convolutional Neural Networks (CNNs), have…
Accurate prediction of cardiovascular disease (CVD) risk is crucial for healthcare institutions. This study addresses the growing prevalence of diabetes and its strong link to heart disease by proposing an efficient CVD risk prediction…
Cardiovascular disease (CVD) remains the foremost cause of mortality worldwide, underscoring the urgent need for intelligent and data-driven diagnostic tools. Traditional predictive models often struggle to generalize across heterogeneous…
Chronic kidney disease (CKD) affects millions worldwide and progresses irreversibly through stages culminating in end-stage renal disease (ESRD) and death. Outcome trials in CKD traditionally employ time-to-first-event analyses using the…
The ongoing COVID-19 pandemic continues to pose significant challenges to global public health, despite the widespread availability of vaccines. Early detection of the disease remains paramount in curbing its transmission and mitigating its…
Kidney stones are a common and debilitating health issue, and genetic factors play a crucial role in determining susceptibility. While Genome-Wide Association Studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs)…
Background: Acute kidney injury (AKI) is a common complication in hospitalized patients and a common cause for chronic kidney disease (CKD) and increased hospital cost and mortality. By timely detection of AKI and AKI progression, effective…
Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely treatment and management. Although population-based CVD risk…
This study explores the potential of utilizing administrative claims data, combined with advanced machine learning and deep learning techniques, to predict the progression of Chronic Kidney Disease (CKD) to End-Stage Renal Disease (ESRD).…
Diabetes remains a significant health challenge globally, contributing to severe complications like kidney disease, vision loss, and heart issues. The application of machine learning (ML) in healthcare enables efficient and accurate disease…