AIMC Topic: Machine Learning

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SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach.

Scientific reports
Sports injury prediction is crucial for university football player health, yet existing research predominantly focuses on professional athletes and lacks interpretability. Using the Kaggle "University Football Injury Prediction Dataset" (800 Chinese ...

Computational analysis on the influence of pressure and temperature on drug solubility in supercritical CO with machine learning and optimizer.

Scientific reports
Machine learning models can be applied for estimation of continuous manufacturing parameters in pharmaceutical processing of oral-solid formulations. Development of Quality by Design (QbD) has motivated the pharmaceutical sector to move towards conti...

Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.

Scientific reports
This study proposes a novel machine learning (ML)-based stacking technique that integrates Single Nucleotide Polymorphisms (SNPs) and inferred local ancestry (LA) to improve predictive accuracy in clinical outcomes. Asthma, particularly severe asthma...

Machine learning prediction of STEMI incidence with SHAP interpretation of environmental determinants.

Scientific reports
ST-segment elevation myocardial infarction (STEMI) is a life-threatening cardiovascular event influenced by meteorological conditions and air pollution. Traditional statistical methods often fail to capture the complex, nonlinear relationships betwee...

Data-augmented machine learning for personalized carbohydrate-protein supplement recommendation for endurance.

Scientific reports
Carbohydrate-protein supplementation often improves endurance performance. However, effectiveness varies significantly among individuals due to unique personal characteristics. This study aimed to develop a predictive machine learning framework for p...

Predicting drug solubility in supercritical carbon dioxide green solvent using machine learning models based on thermodynamic properties.

Scientific reports
Reliable prediction of drug solubility in supercritical carbon dioxide (scCO₂) is crucial for the efficient design of pharmaceutical processes, including particle engineering and supercritical fluid-based extraction. Given that experimental determina...

Automated meningioma detection using skull X ray images with deep learning and machine learning classifiers.

Scientific reports
This study aimed to develop a novel diagnostic tool for detecting meningioma using skull X-ray images, combining deep learning with traditional machine learning classifiers. The goal was to explore the potential of using a cost-effective and widely a...

Enhancing image based classification for crop disease detection using a multiclass SVM approach with kernel comparison.

Scientific reports
Agricultural production is still quite susceptible to plant diseases, despite the fact that it is essential to both economic growth and food security. Yellow rust can lower wheat yields by 20-30%, red rust by 5-10%, and anthracnose by up to 60% in cr...

Fungal virulence factors datasets for inflammatory bowel disease-specific antifungal drug discovery.

Scientific data
Fungi are closely associated with various diseases, among which Candida albicans (C. albicans) is recognized as an important pathogen in inflammatory bowel disease (IBD). Fungal pathogenicity is primarily mediated by virulence factors (VFs); therefor...

Subtyping schizophrenia via machine learning by using structural neuroimaging.

Translational psychiatry
Schizophrenia is a heterogeneous disorder with diverse clinical presentations and neuroanatomical alterations. Despite recent advances, we still lack a working hypothesis for the pathophysiology of schizophrenia. One reason might be the heterogeneous...