AIMC Topic: Machine Learning

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Correlation enhanced distribution adaptation for prediction of fall risk.

Scientific reports
With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older a...

Ensemble learning based transmission line fault classification using phasor measurement unit (PMU) data with explainable AI (XAI).

PloS one
A large volume of data is being captured through the Phasor Measurement Unit (PMU), which opens new opportunities and challenges to the study of transmission line faults. To be specific, the Phasor Measurement Unit (PMU) data represents many differen...

Classification of inertial sensor-based gait patterns of orthopaedic conditions using machine learning: A pilot study.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making. Therefore, we investigated whether gait pattern...

Machine learning-based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive coronary angiography.

European radiology
OBJECTIVES: Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance.

Machine Learning Models for Low Back Pain Detection and Factor Identification: Insights From a 6-Year Nationwide Survey.

The journal of pain
This study aimed to enhance performance, identify additional predictors, and improve the interpretability of biopsychosocial machine learning models for low back pain (LBP). Using survey data from a 6-year nationwide study involving 17,609 adults age...

A comprehensive and reliable feature attribution method: Double-sided remove and reconstruct (DoRaR).

Neural networks : the official journal of the International Neural Network Society
The limited transparency of the inner decision-making mechanism in deep neural networks (DNN) and other machine learning (ML) models has hindered their application in several domains. In order to tackle this issue, feature attribution methods have be...

Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods.

Behavior genetics
Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to e...

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.

Sensors (Basel, Switzerland)
Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice,...