Archives

Applied Health Technology Research Conference at Scandic Karlskrona, December 11th, 2023

The Applied Health Technology Research Conference at Blekinge Institute of Technology was a vibrant convergence of minds at Scandic Karlskrona on December 11th, 2023. Researchers and professionals immersed themselves in the latest breakthroughs at the intersection of technology and healthcare. The conference provided a platform for unveiling groundbreaking studies and innovations, ranging from telemedicine solutions […]

Read More

Empowering Dementia Diagnosis: A Machine Learning-Driven Automated System

Abstract: Dementia, a neurodegenerative disease, significantly impairs cognitive abilities and is often not diagnosed until the later stages of disease progression. This delayed diagnosis results in missed early intervention, support opportunities and difficulty implementing appropriate care strategies. To address this problem, researchers have proposed automated diagnostic systems that utilize machine learning methods using electronic health […]

Read More

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Abstract Background:Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological […]

Read More
Proposed_Model

Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning

Abstract : Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental […]

Read More

Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification

Abstract Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using […]

Read More

Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions

Abstract Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia […]

Read More
Schematic overview of the proposed intelligent learning system for dementia prediction

An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning

Abstract Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchers have presented several methods for early detection of dementia […]

Read More

The DiaVoc project: Diagnosing vocal characteristics to track patients’ health

This project centers on the diagnosis and monitoring of health conditions that impact patients’ vocal characteristics, including Neurocognitive disorders (NCDs) (signifying cognitive decline), pulmonary disorder (COPD), and heart failure conditions (HF). By utilizing longitudinal voice recordings paired with medical data, we aim to create mathematical vocal characteristics, distance metrics, and machine learning methodologies that are […]

Read More