AIMC Topic: Support Vector Machine

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Decoding objects of basic categories from electroencephalographic signals using wavelet transform and support vector machines.

Brain topography
Decoding and classification of objects through task-oriented electroencephalographic (EEG) signals are the most crucial goals of recent researches conducted mainly for brain-computer interface applications. In this study we aimed to classify single-t...

A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies.

Statistical methods in medical research
BACKGROUND: Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimat...

Combining extreme learning machines using support vector machines for breast tissue classification.

Computer methods in biomechanics and biomedical engineering
In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. Th...

Adaptive modelling approach for predicting causes of death: insights from verbal autopsy data in Tanzania.

International health
BACKGROUND: The World Health Organization (WHO) has approved the use of a verbal autopsy (VA), a survey-based approach to generate out-of-hospital causes of death (CoDs). Through this study, an adaptive Bayesian networks machine learning model was de...

Machine learning based diagnostics of veterinary cancer on ultrasound and optical imaging data.

The veterinary quarterly
Study advances current diagnostic efficiency of canine/feline (sub-)cutaneous tumors using machine learning and multimodal imaging data. White light (WL), fluorescence (FL) and ultrasound (US) imaging were combined into hybrid approaches to different...

Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool.

Annals of medicine
BACKGROUND: Adequate bowel preparation is crucial for effective colonoscopy, especially in elderly patients who face a high risk of inadequate preparation. This study develops and validates a machine learning model to predict bowel preparation adequa...

An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach.

Systems biology in reproductive medicine
Infertility has emerged as a significant public health concern, with assisted reproductive technology (ART) is a last-resort treatment option. However, ART's efficacy is limited by significant financial cost and physical discomfort. The aim of this s...

Predictive efficacy of machine-learning algorithms on intrahepatic cholestasis of pregnancy based on clinical and laboratory indicators.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
OBJECTIVES: Intrahepatic cholestasis of pregnancy (ICP), a condition exclusive to pregnancy, necessitates prompt identification and intervention to improve the perinatal outcomes. This study aims to develop suitable machine-learning models for predic...

Beyond model-specific biases: An explainable multifaceted approach for robust PM source apportionment.

Environmental research
Liu et al. (2025) present an innovative approach to PM source apportionment in urban environments by integrating Positive Matrix Factorization with machine learning (ML) models including XGBoost, Random Forest (RF), and Support Vector Machine (SVM). ...

Enhancing differentiation between unipolar and bipolar depression through integration of machine learning and electroencephalogram analysis.

Journal of affective disorders
To enhance the differentiation between unipolar depression (UPD) and bipolar depression (BPD), this study integrates machine learning and deep learning models with electroencephalography (EEG) data and clinical features. Utilizing Python for data pre...