AIMC Topic: Machine Learning

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A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach.

PloS one
Tennis, a widely enjoyed sport, motivates athletes and coaches to optimize training for competitive success. This retrospective predictive study examines anthropometric features and statistics of 1990 tennis players in the 2022 season, using 20,040 d...

Sex estimation from patellar measurements in a contemporary Italian population: a machine learning approach.

International journal of legal medicine
Biological sex estimation in forensic anthropology is a crucial topic, and the patella has shown promise in this regard due to its sexual dimorphism. This study uses 12 machine learning models for sex estimation based on three patellar measurements (...

Machine learning models for predicting dysphonia following anterior cervical discectomy and fusion: a Swedish Registry Study.

The spine journal : official journal of the North American Spine Society
BACKGROUND: Dysphonia is one of the more common complications following anterior cervical discectomy and fusion (ACDF). ACDF is the gold standard for treating degenerative cervical spine disorders, and identifying high-risk patients is therefore cruc...

FedART: A neural model integrating federated learning and adaptive resonance theory.

Neural networks : the official journal of the International Neural Network Society
Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed clients while preserving data privacy. However, prevailing FL approaches aggregate the clients' local models into a global model through m...

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

Epilepsia
OBJECTIVE: This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features.

Evaluating machine learning model bias and racial disparities in non-small cell lung cancer using SEER registry data.

Health care management science
BACKGROUND: Despite decades of pursuing health equity, racial and ethnic disparities persist in healthcare in America. For cancer specifically, one of the leading observed disparities is worse mortality among non-Hispanic Black patients compared to n...

Enhancing SARS-CoV-2 Lineage Surveillance through the Integration of a Simple and Direct qPCR-Based Protocol Adaptation with Established Machine Learning Algorithms.

Analytical chemistry
Emerging and evolving Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) lineages, adapted to changing epidemiological conditions, present unprecedented challenges to global public health systems. Here, we introduce an adapted analytical ap...

Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model.

Sensors (Basel, Switzerland)
To enhance the classification accuracy of lower limb movements, a fusion recognition model integrating a surface electromyography (sEMG)-based convolutional neural network, transformer encoder, and long short-term memory network (CNN-Transformer-LSTM...

Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models.

European journal of medical research
OBJECTIVES: Our research aims to construct machine learning prediction models to identify patients proned to recurrence after inverted papilloma (IP) surgery and guide their follow-up treatment.

Construction of a Wilms tumor risk model based on machine learning and identification of cuproptosis-related clusters.

BMC medical informatics and decision making
BACKGROUND: Cuproptosis, a recently identified type of programmed cell death triggered by copper, has mechanisms in Wilms tumor (WT) that are not yet fully understood. This research focuses on examining the link between WT and Cuproptosis-related gen...