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This study presents a web-based interactive health risk prediction tool designed to assess diabetes risk using machine learning models. Built on the 2015 CDC BRFSS dataset, the study evaluates models including Logistic Regression, Random…

In many nations, diabetes is becoming a significant health problem, and early identification and control are crucial. Using machine learning algorithms to predict diabetes has yielded encouraging results. Using the Pima Indians Diabetes…

Leveraging health administrative data (HAD) datasets for predicting the risk of chronic diseases including diabetes has gained a lot of attention in the machine learning community recently. In this paper, we use the largest health records…

Diabetes impacts the quality of life of millions of people. However, diabetes diagnosis is still an arduous process, given that the disease develops and gets treated outside the clinic. The emergence of wearable medical sensors (WMSs) and…

Diabetes, resulting from inadequate insulin production or utilization, causes extensive harm to the body. Existing diagnostic methods are often invasive and come with drawbacks, such as cost constraints. Although there are machine learning…

The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring. Despite advances in multi-sensor fusion, a…

Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are…

This project addresses the need for efficient, real-time analysis of biomedical signals such as electrocardiograms (ECG) and electroencephalograms (EEG) for continuous health monitoring. Traditional methods rely on long-duration data…

Type 2 Diabetes Mellitus (T2DM) remains a global health challenge, underscoring the need for early and accurate risk prediction. This study presents ECG-DiaNet, a multimodal deep learning model that integrates electrocardiogram (ECG)…

This study develops a cloud-based deep learning system for early prediction of diabetes, leveraging the distributed computing capabilities of the AWS cloud platform and deep learning technologies to achieve efficient and accurate risk…

We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent…

In the U.S., over a third of adults are pre-diabetic, with 80\% unaware of their status. This underlines the need for better glucose monitoring to prevent type 2 diabetes and related heart diseases. Existing wearable glucose monitors are…

Diabetes mellitus is a chronic metabolic disorder that has emerged as one of the major health problems worldwide due to its high prevalence and serious complications, which are pricey to manage. Effective management requires good glycemic…

Electromyography (EMG) refers to a biomedical signal indicating neuromuscular activity and muscle morphology. Experts accurately diagnose neuromuscular disorders using this time series. Modern data analysis techniques have recently led to…

Time series forecasting is a fundamental task emerging from diverse data-driven applications. Many advanced autoregressive methods such as ARIMA were used to develop forecasting models. Recently, deep learning based methods such as DeepAr,…

Diabetes is a prevalent chronic condition that compromises the health of millions of people worldwide. Minimally invasive methods are needed to prevent and control diabetes but most devices for measuring glucose levels are invasive and not…

This study introduces a novel approach for early Type 2 Diabetes Mellitus (T2DM) risk prediction using a tabular transformer (TabTrans) architecture to analyze longitudinal patient data. By processing patients` longitudinal health records…

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in…

The global diabetes epidemic highlights the importance of maintaining good glycemic control. Glucose prediction is a fundamental aspect of diabetes management, facilitating real-time decision-making. Recent research has introduced models…

This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy…