AIMC Topic: Support Vector Machine

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Decision support system based on ensemble models in distinguishing epilepsy types.

Epilepsy & behavior : E&B
This study aimed to classify patients' focal (frontal, temporal, parietal, occipital), multifocal, and generalized epileptiform activities based on EEG findings using artificial intelligence models. The study included 575 patients followed in the Neu...

Lyophilized nasal swabs for COVID-19 detection by ATR-FTIR spectroscopy: Machine learning-based approach.

Biophysical chemistry
The COVID-19 pandemic continues to pose challenges for global health. The disease burden and diagnostic pressure has forced scientists to explore alternate diagnostic tools beyond the standard PCR testing. One such promising tool is the use of spectr...

ResNeXt-Based Rescoring Model for Proteoform Characterization in Top-Down Mass Spectra.

Interdisciplinary sciences, computational life sciences
In top-down proteomics, the accurate identification and characterization of proteoform through mass spectrometry represents a critical objective. As a result, achieving accuracy in identification results is essential. Multiple primary structure alter...

Pseudotargeted metabolomics profiles potential damage-associated molecular patterns as machine learning predictors for acute pancreatitis.

Journal of pharmaceutical and biomedical analysis
Acute pancreatitis (AP) is a common gastrointestinal disease characterized by pancreatic cell damage and inflammation. Given the early clinical diagnosis and management challenges, exploring novel analytical frameworks from new orientations for inter...

Machine Learning Model for Predicting Sertraline-like Activities and Its Impact on Cancer Chemosensitization.

ACS chemical neuroscience
Selective serotonin reuptake inhibitors (SSRIs) like sertraline are crucial in treating depression and anxiety disorders, and studies indicate their potential as chemosensitizers in cancer therapy. This research develops a machine-learning predictive...

[Development of a machine learning-based diagnostic model for T-shaped uterus using transvaginal 3D ultrasound quantitative parameters].

Zhonghua yi xue za zhi
To develop a machine learning diagnostic model for T-shaped uterus based on quantitative parameters from 3D transvaginal ultrasound. A retrospective cross-sectional study was conducted, recruiting 304 patients who visited the hysteroscopy centre of...

A Proof-of-Concept Development on Speech Analysis for Concussion Detection.

Studies in health technology and informatics
Speech signal analysis to support objective clinical decision-making has gained immense interest, especially in neurological disorders. This research assessed the feasibility of speech analysis on the detection of concussions. Using a speech dataset ...

Vibrational spectroscopy of body fluids combined with machine learning for the early diagnosis of cystic echinococcosis.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Cystic echinococcosis (CE) is a globally prevalent zoonotic parasitic disease. Due to the covert symptoms and the inadequacies of screening technologies, accurate early diagnosis is crucial. This study explores the feasibility of employing body fluid...

Sex-estimation method for three-dimensional shapes of the skull and skull parts using machine learning.

Forensic science international
Sex estimation is an indispensable test for identifying skeletal remains in the field of forensic anthropology. We developed a novel sex-estimation method for skulls and several parts of the skull using machine learning. A total of 240 skull shapes w...

Analysis of the neural mechanisms of social anxiety based on EEG features and machine learning and construction of a diagnostic model.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology
Social anxiety is a common psychological problem, and its accurate diagnosis and investigation of underlying neurophysiological mechanisms are of significant importance. This study aims to explore the neuroelectrophysiological characteristics and dia...