AIMC Topic: Support Vector Machine

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Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants.

Scientific reports
This study presents a novel approach for the classification of coronavirus species and variants of SARS-CoV-2 using Chaos Game Representation (CGR) and 2D Multifractal Detrended Fluctuation Analysis (2D MF-DFA). By extracting fractal parameters from ...

Suicide risk prediction for Korean adolescents based on machine learning.

Scientific reports
Traditional clinical risk assessment tools proved inadequate for reliably identifying individuals at high risk for suicidal behavior. As a result, machine learning (ML) techniques have become progressively incorporated into psychiatric care. This stu...

A novel diagnosis method utilizing MDBO-SVM and imaging genetics for Alzheimer's disease.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Alzheimer's disease (AD) is the most common neurodegenerative disorder, yet its underlying mechanisms remain elusive. Early and accurate diagnosis is crucial for timely intervention and disease management. In this paper, a multi-strategy improved dun...

Hybrid Neural network and machine learning models with improved optimization method for gut microbiome effects on the sleep quality in patients with endometriosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Endometriosis is a chronic gynecological condition known to affect the quality of life of millions of women globally, often manifesting with symptoms that impact sleep quality. Emerging evidence suggests a crucial role of th...

Altered static and dynamic functional network connectivity and combined Machine learning in asthma.

Neuroscience
Asthma is a reversible disease characterized by airflow limitation and chronic airway inflammation. Previous neuroimaging studies have shown structural and functional abnormalities in the brains of individuals with asthma. However, earlier research h...

Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction.

Frontiers in public health
INTRODUCTION: Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and time factors. Machine learning offers powerful tools...

Modeling the number of new cases of childhood type 1 diabetes using Poisson regression and machine learning methods; a case study in Saudi Arabia.

PloS one
Diabetes mellitus stands out as one of the most prevalent chronic conditions affecting pediatric populations. The escalating incidence of childhood type 1 diabetes (T1D) globally is a matter of increasing concern. Developing an effective model that l...

A comparative study of neural network architectures for vital signs monitoring based on the national early warning systems (NEWS).

Health informatics journal
The study aims to assess the efficacy of various neural network architectures in predicting the National Early Warning Systems (NEWS) score, using vital signs, to enhance early warning and monitoring in clinical settings. A comparative evaluation o...

Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN).

Scientific reports
The ambulance dispatch system plays a crucial role in emergency medical care by ensuring efficient communication, reducing response times, and ultimately saving lives. Delays in ambulance arrival can have serious consequences for patient health and s...

EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA.

Scientific reports
This study focuses on epilepsy detection using hybrid CNN-SVM and DNN-SVM models, combined with feature dimensionality reduction through PCA. The goal is to evaluate the effectiveness and performance of these models in accurately identifying epilepti...