AIMC Topic: Machine Learning

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Machine Learning-Based Analysis of Lifestyle Risk Factors for Atherosclerotic Cardiovascular Disease: Retrospective Case-Control Study.

JMIR medical informatics
BACKGROUND: The risk of developing atherosclerotic cardiovascular disease (ASCVD) varies among individuals and is related to a variety of lifestyle factors in addition to the presence of chronic diseases.

AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
We developed an adaptive and unified stacking genomic selection framework and designed a model interpretation strategy to identify the candidate significant SNPs of target traits. Genomic selection (GS) is an important technique in modern molecular b...

Reducing bias in coronary heart disease prediction using Smote-ENN and PCA.

PloS one
Coronary heart disease (CHD) is a major cardiovascular disorder that poses significant threats to global health and is increasingly affecting younger populations. Its treatment and prevention face challenges such as high costs, prolonged recovery per...

Multi-modal machine learning classifier for idiopathic pulmonary fibrosis predicts mortality in interstitial lung diseases.

Respiratory investigation
BACKGROUND: Interstitial lung disease (ILD) prognostication incorporates clinical history, pulmonary function testing (PFTs), and chest CT pattern classifications. The machine learning classifier, Fibresolve, includes a model to help detect CT patter...

Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records.

Scientific reports
Rare diseases, such as Mucopolysaccharidosis (MPS), present significant challenges to the healthcare system. Some of the most critical challenges are the delay and the lack of accurate disease diagnosis. Early diagnosis of MPS is crucial, as it has t...

Machine learning training data: over 500,000 images of butterflies and moths (Lepidoptera) with species labels.

Scientific data
Deep learning models can accelerate the processing of image-based biodiversity data and provide educational value by giving direct feedback to citizen scientists. However, the training of such models requires large amounts of labelled data and not al...

Invulnerability bias in perceptions of artificial intelligence's future impact on employment.

Scientific reports
The adoption of Artificial Intelligence (AI) is reshaping the labor market; however, individuals' perceptions of its impact remain inconsistent. This study investigates the presence of the Invulnerability Bias (IB), where workers perceive that AI wil...

Alzheimer's disease risk prediction using machine learning for survival analysis with a comorbidity-based approach.

Scientific reports
Alzheimer's disease (AD) presents a pressing global health challenge, demanding improved strategies for early detection and understanding its progression. In this study, we address this need by employing survival analysis techniques to predict transi...

Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers.

Scientific reports
Traditional diagnostic methods for asthma, a widespread chronic respiratory illness, are often limited by factors such as patient cooperation with spirometry. Non-invasive acoustic analysis using machine learning offers a promising alternative for ob...

Machine learningdriven framework for realtime air quality assessment and predictive environmental health risk mapping.

Scientific reports
This research introduces a practical and innovative approach for real-time air quality assessment and health risk prediction, focusing on urban, industrial, suburban, rural, and traffic-heavy environments. The framework integrates data from multiple ...