AIMC Topic: Support Vector Machine

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Machine learning characterization of a rare neurologic disease via electronic health records: a proof-of-principle study on stiff person syndrome.

BMC neurology
BACKGROUND: Despite the frequent diagnostic delays of rare neurologic diseases (RND), it remains difficult to study RNDs and their comorbidities due to their rarity and hence the statistical underpowering. Affecting one to two in a million annually, ...

Predicting the Hallucinogenic Potential of Molecules Using Artificial Intelligence.

ACS chemical neuroscience
The development of new drugs addressing serious mental health and other disorders should avoid the psychedelic experience. Analogs of psychedelic drugs can have clinical utility and are termed "psychoplastogens". These represent promising candidates ...

Identifying diseases symptoms and general rules using supervised and unsupervised machine learning.

Scientific reports
The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. Machine learning has made major adv...

Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran.

Environmental science and pollution research international
Floods are among the natural hazards that have seen a rapid increase in frequency in recent decades. The damage caused by floods, including human and financial losses, poses a serious threat to human life. This study evaluates two machine learning (M...

From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke.

GeroScience
Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarke...

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.

Scientific reports
Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It...

Soil organic carbon estimation using remote sensing data-driven machine learning.

PeerJ
Soil organic carbon (SOC) is a crucial component of the global carbon cycle, playing a significant role in ecosystem health and carbon balance. In this study, we focused on assessing the surface SOC content in Shandong Province based on land use type...

A MOF-on-MOF heterostructure ratiometric/colorimetric dual-mode fluorescence sensor based on support vector machine for detecting tetracyclines in animal-derived foods.

Food chemistry
The misuse of tetracyclines in livestock production poses significant health risks. Thus, establishing convenient detection methods to replace complex laboratory tests for food safety is crucial. In this study, a heterostructure Zn-BTC/IRMOF-3 (denot...

Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
OBJECTIVES: Radiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine-learning (ML) model and evaluate its effectiveness in detecting oral squamous cell carcinoma (OSCC)...

Modeling the Impact of Ergonomic Interventions and Occupational Factors on Work-Related Musculoskeletal Disorders in the Neck of Office Workers with Machine Learning Methods.

Journal of research in health sciences
BACKGROUND: Modeling with methods based on machine learning (ML) and artificial intelligence can help understand the complex relationships between ergonomic risk factors and employee health. The aim of this study was to use ML methods to estimate the...