AIMC Topic: Machine Learning

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Integrating miRNA profiling and machine learning for improved prostate cancer diagnosis.

Scientific reports
Prostate cancer (PCa) diagnosis remains challenging due to overlapping clinical features with benign prostatic hyperplasia (BPH) and limitations of existing diagnostic tools like PSA tests, which yield high false-positive rates. This study investigat...

Predicting in-hospital mortality in ICU patients with lymphoma using machine learning models.

PloS one
BACKGROUND: Lymphoma is a severe condition with high mortality rates, often requiring ICU admission. Traditional risk stratification tools like SOFA and APACHE scores struggle to capture complex clinical interactions. Machine learning (ML) models off...

Classification and quantification of sesame oil in edible oils and adulterated mixtures using H NMR spectroscopy combined with multivariate, machine learning, and deep learning models.

Food chemistry
Sesame oil is often adulterated with cheaper oils, necessitating accurate authentication and quantification methods. This study investigates the performance of AI-based models using H NMR spectral data for edible oil classification and sesame oil qua...

Simulation, prediction and optimization of heavy metal adsorption by metal-organic frameworks with machine learning.

Environmental research
The unique structures and complex characteristics of Metal-organic frame (MOFs) obscure understanding the processes and mechanisms of heavy metal (HM) removal. This study established an interpretable machine learning (ML) framework predicting adsorpt...

Machine Learning-Driven Dual-Recognition Magnetic Imprinted Polymers: Host-Guest/Aptamer Synergy Enabling Ultrasensitive Chloramphenicol Detection.

Analytical chemistry
To address the challenges in detecting chloramphenicol (CAP) in complex food matrices, this study developed a magnetic solid-phase microextraction coupled with a high-performance liquid chromatography (MSPME-HPLC) system that integrates machine learn...

A machine learning approach to predict self-efficacy in breast cancer survivors.

BMC medical informatics and decision making
PURPOSE: To determine predictors of self-efficacy in breast cancer survivors and identify vulnerable groups.

Predictive value of anthropometric indices for incident of dyslipidemia: a large population-based study.

Population health metrics
INTRODUCTION: Dyslipidemia as a modifiable risk factor for chronic non-communicable diseases has become a worldwide concern. We aim to explore different anthropometric measures as predictors of dyslipidemia using various machine learning methods.

Construction of a feature gene and machine prediction model for inflammatory bowel disease based on multichip joint analysis.

Journal of translational medicine
BACKGROUND: Inflammatory bowel disease (IBD) is a chronic nonspecific inflammatory disorder triggered by immune responses and genetic factors. Currently, there is no cure for IBD, and its etiology remains unclear. As a result, early detection and dia...

Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification.

Scientific reports
Eye diseases can affect vision and well-being, so early, accurate diagnosis is crucial to prevent serious impairment. Deep learning models have shown promise for automating the diagnosis of eye diseases from images. However, current methods mostly us...

Optimized machine learning based comparative analysis of predictive models for classification of kidney tumors.

Scientific reports
The kidney is an important organ that helps clean the blood by removing waste, extra fluids, and harmful substances. It also keeps the balance of minerals in the body and helps control blood pressure. But if the kidney gets sick, like from a tumor, i...