AIMC Topic: Support Vector Machine

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Deep Learning-Based Diagnostic Model for Parkinson's Disease Using Handwritten Spiral and Wave Images.

Current medical science
OBJECTIVE: To develop and validate a deep neural network (DNN) model for diagnosing Parkinson's Disease (PD) using handwritten spiral and wave images, and to compare its performance with various machine learning (ML) and deep learning (DL) models.

MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.

Scientific reports
We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene...

Diffusion-Weighted Imaging-Based Radiomics Features and Machine Learning Method to Predict the 90-Day Prognosis in Patients With Acute Ischemic Stroke.

The neurologist
OBJECTIVES: The evaluation of the prognosis of patients with acute ischemic stroke (AIS) is of great significance in clinical practice. We aim to evaluate the feasibility and effectiveness of diffusion-weighted imaging (DWI) image-based radiomics fea...

Predicting responsiveness to fixed-dose methylene blue in adult patients with septic shock using interpretable machine learning: a retrospective study.

Scientific reports
This study aimed to develop an interpretable machine learning model to predict methylene blue (MB) responsiveness in adult patients with refractory septic shock and to identify key factors influencing MB responsiveness using the SHapley Additive exPl...

Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features.

Aging clinical and experimental research
OBJECTIVES: Sarcopenic obesity (SO), characterized by the coexistence of obesity and sarcopenia, is an increasingly prevalent condition in aging populations, associated with numerous adverse health outcomes. We aimed to identify and validate an expla...

Machine Learning-Based Identification of Novel Exosome-Derived Metabolic Biomarkers for the Diagnosis of Systemic Lupus Erythematosus and Differentiation of Renal Involvement.

Current medical science
OBJECTIVE: This study aims to investigate the exosome-derived metabolomicsĀ profiles in systemic lupus erythematosus (SLE), identify differential metabolites, and analyze their potential as diagnostic markers for SLE and lupus nephritis (LN).

Application of Machine Learning for FOS/TAC Soft Sensing in Bio-Electrochemical Anaerobic Digestion.

Molecules (Basel, Switzerland)
This study explores the application of various machine learning (ML) models for the real-time prediction of the FOS/TAC ratio in microbial electrolysis cell anaerobic digestion (MEC-AD) systems using data collected during a 160-day trial treating bre...

Enhancing e-waste management: a novel light gradient AdaBoost support vector classification approach.

Environmental monitoring and assessment
The global consequences of electronic waste significantly affect the environment and human health. Accurate classification is essential for effective recycling and management to mitigate serious environmental harm caused by improper disposal. However...

Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture.

Clinical interventions in aging
BACKGROUND: Hip fractures have become a significant health concern, particularly among super-aged patients, who were at a high risk of postoperative pneumonia due to their frailty and the presence of multiple comorbidities. This study aims to establi...

Prediction and validation of anoikis-related genes in neuropathic pain using machine learning.

PloS one
BACKGROUND: Neuropathic pain (NP) can be induced by a variety of clinical conditions, such as spinal cord injury, lumbar disc herniation (LDH), lumbar spinal stenosis, diabetes, herpes zoster, and spinal cord tumors, and inflammatory stimuli. The pat...