AIMC Topic: Machine Learning

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Machine learning-based CAD detection using integrated ECG and PCG parameter features.

Biomedical physics & engineering express
The combined analysis of electrocardiogram (ECG) and phonocardiogram signals(PCG) has demonstrated significant potential in the non-invasive detection of coronary artery disease (CAD). The efficacy of combining cardiac pathological parameters such as...

Improving the accuracy of cybersecurity spam email detection using ensemble techniques: A stacking approach Machine learning for spam email detection.

PloS one
With the widespread adoption of internet technologies and email communication systems, the exponential growth in email usage has precipitated a corresponding surge in spam proliferation. These unsolicited messages not only consume users' valuable tim...

Explainable mortality prediction models incorporating social health determinants and physical frailty for heart failure patients.

PloS one
There is limited evidence on how social determinants of health (SDOH) and physical frailty (PF) influence mortality prediction in heart failure (HF), particularly for in-hospital, 90-day, and 1-year outcomes. This study aims to develop explainable ma...

AI-driven analysis of diabetes risk determinants in U.S. adults: Exploring disease prevalence and health factors.

PloS one
BACKGROUND: Diabetes remains a major public health concern in the United States, with a complex interplay of behavioral, demographic, and clinical risk factors. This study aims to identify the three best-performing machine learning models for diabete...

Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation.

Annals of medicine
BACKGROUND: Acute kidney injury (AKI) is a prevalent and life-threatening condition characterized by abrupt renal function decline and subsequent inflammatory cascades. PANoptosis has emerged as a significant contributor to the pathophysiology of AKI...

Machine learning-driven discovery of multicomponent pharmaceutical solid forms via DualNet: confidence-aware prediction and ranking of salts and cocrystals.

International journal of pharmaceutics
Salts and cocrystals are vital multicomponent entities for tuning pharmaceuticals' solid-state properties, yet their experimental screening is labor-intensive and often inefficient. We introduce a DualNet Ensemble algorithm, a multi-class classificat...

Assessing biodegradability potential of organic chemicals in aquatic and soil environment through classification-based machine learning models developed in accordance with OECD standards.

The Science of the total environment
Information on the biodegradation potential of organic chemicals in the ecosystem helps us analyze their persistence, bioaccumulation, and toxicity (PBT) behaviour. The environment is exposed to many chemicals from various sources, both intentionally...

Chemical Space Exploration with Artificial "Mindless" Molecules.

Journal of chemical information and modeling
We introduce MindlessGen, a Python-based generator for creating chemically diverse, "mindless" molecules through random atomic placement and subsequent geometry optimization. Using this framework, we constructed the benchmark set, containing 2061 mo...

Frontiers Shaping the Next Generation of Transformation Product Prediction and Toxicological Assessment.

Environmental science & technology
The characterization of transformation products (TPs) is crucial for understanding chemical fate and potential environmental hazards. TPs form through (a)biotic processes and can be detected in environmental concentrations comparable to or even excee...

Single-cell sequencing analysis and multiple machine learning methods identified immune-associated SERPINB1 and CPEB4 as novel biomarkers for COVID-19-induced ARDS.

Die Naturwissenschaften
Acute respiratory distress syndrome (ARDS) is a life-threatening complication of COVID-19, often resulting in respiratory failure and high mortality. Identifying effective molecular biomarkers is crucial for understanding its pathogenesis and improvi...