AIMC Topic: Machine Learning
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An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data.
BMC cancer
Jul 16, 2025
BACKGROUND: The accurate early-stage diagnosis of gallbladder cancer (GBC) is regarded as one of the major challenges in the field of oncology. However, few studies have focused on the comprehensive classification of GBC based on multiple modalities....
A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization.
Scientific reports
Jul 16, 2025
With human guidance, computers now use machine learning (ML) in artificial intelligence (AI) to learn from data, detect trends, and make predictions. Software can adapt and improve with new information. Imaging scans leverage pattern recognition to p...
Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network.
Scientific reports
Jul 16, 2025
This study explores the impact mechanism of college students' sports behavior on their well-being by constructing an Artificial Neural Network (ANN) model. The study employs an ANN architecture that combines a Long Short-Term Memory (LSTM) network an...
Machine learning to evaluate the effects of non-clinical social determinant features in predicting colorectal Cancer mortality in a medically underserved Appalachian population.
Scientific reports
Jul 16, 2025
Colorectal cancer (CRC) is the 2nd leading cause of cancer death in the United States (US). Rural Appalachia suffers the highest CRC incidence and mortality rates. There are several non-clinical health-related social determinant factors (SDOH) associ...
New hybrid features extracted from US images for breast cancer classification.
Scientific reports
Jul 16, 2025
Artificial intelligence (AI), and image processing fields play a vital role in classifying benign and malignant breast cancer (BC). The novelty of this paper lies in computing original hybrid features (HF) from textural and shape features of BC integ...
Developing an explainable machine learning and fog computing-based visual rating scale for the prediction of dementia progression.
Scientific reports
Jul 16, 2025
Recently, dementia research has primarily concentrated on using Magnetic Resonance Imaging (MRI) to develop learning models in processing and analyzing brain data. However, these models often cannot provide early detection of affected brain regions. ...
In Silico tool for predicting, designing and scanning IL-2 inducing peptides.
Scientific reports
Jul 16, 2025
Interleukin-2 (IL-2) based immunotherapy has been approved for treating certain types of cancer, as IL-2 plays a crucial role in regulating the immune system. In this study, we developed a method for predicting IL-2-inducing peptides. Our method was ...
Multi-scale machine learning model predicts muscle and functional disease progression.
Scientific reports
Jul 16, 2025
Facioscapulohumeral muscular dystrophy (FSHD) is a genetic neuromuscular disorder characterized by progressive muscle degeneration with substantial variability in severity and progression patterns. FSHD is a highly heterogeneous disease; however, cur...
Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000-2019).
BMJ open
Jul 16, 2025
OBJECTIVE: Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to fun...
Machine learning model for postpancreaticoduodenectomy haemorrhage prediction: an international multicentre cohort study.
BMJ open
Jul 16, 2025
OBJECTIVES: To develop and validate a machine learning model for precise risk stratification of postpancreaticoduodenectomy haemorrhage (PPH), enabling early identification of high-risk patients to guide clinical intervention.
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