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Evaluating canine electrocardiograms (ECG) require skilled veterinarians, but current availability of veterinary cardiologists for ECG interpretation and diagnostic support is limited. Developing tools for automated assessment of ECG…
This study presents an application of machine learning (ML) methods for detecting the presence of stenoses and aneurysms in the human arterial system. Four major forms of arterial disease -- carotid artery stenosis (CAS), subclavian artery…
Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental condition characterized by repetitive behaviors and impairments in social and communication skills. Despite the clear manifestation of these symptoms, many individuals with ASD…
Adherence can be defined as "the extent to which patients take their medications as prescribed by their healthcare providers"[Osterberg and Blaschke, 2005]. World Health Organization's reports point out that, in developed countries, only…
Mental health challenges and cyberbullying are increasingly prevalent in digital spaces, necessitating scalable and interpretable detection systems. This paper introduces a unified multiclass classification framework for detecting ten…
Mental health disorders remain among the leading cause of disability worldwide, yet conditions such as depression, anxiety, and Post-Traumatic Stress Disorder (PTSD) are frequently underdiagnosed or misdiagnosed due to subjective…
Autism Spectrum Disorder (ASD) is often underdiagnosed in females due to gender-specific symptom differences overlooked by conventional diagnostics. This study evaluates machine learning models, particularly Random Forest and convolutional…
Early detection of chronic and Non-Communicable Diseases (NCDs) is crucial for effective treatment during the initial stages. This study explores the application of wearable devices and Artificial Intelligence (AI) in order to predict…
We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issue the most appropriate recommendation in terms of best point…
Veterinary medical records represent a large data resource for application to veterinary and One Health clinical research efforts. Use of the data is limited by interoperability challenges including inconsistent data formats and data…
Deep learning methods based on Convolutional Neural Networks (CNNs) have shown great potential to improve early and accurate diagnosis of Alzheimer's disease (AD) dementia based on imaging data. However, these methods have yet to be widely…
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…
Convolutional neural networks (CNN) have become a powerful tool for detecting patterns in image data. Recent papers report promising results in the domain of disease detection using brain MRI data. Despite the high accuracy obtained from…
AI-powered stethoscopes offer a promising alternative for screening rheumatic heart disease (RHD), particularly in regions with limited diagnostic infrastructure. Early detection is vital, yet echocardiography, the gold standard tool,…
We classify very-mild to moderate dementia in patients (CDR ranging from 0 to 2) using a support vector machine classifier acting on dimensionally reduced feature set derived from MRI brain scans of the 416 subjects available in the…
In healthcare, detecting stress and enabling individuals to monitor their mental health and wellbeing is challenging. Advancements in wearable technology now enable continuous physiological data collection. This data can provide insights…
Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on…
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…
The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for (1)…
We use partial class memberships in soft classification to model uncertain labelling and mixtures of classes. Partial class memberships are not restricted to predictions, but may also occur in reference labels (ground truth, gold standard…