AIMC Topic: Support Vector Machine

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Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms.

European journal of medical research
OBJECTIVES: This study aimed to identify risk factors for diabetic retinopathy (DR) and develop machine learning (ML)-based predictive models using routine laboratory data in patients with type 2 diabetes mellitus (T2DM).

Construction of a machine learning-based interpretable prediction model for acute kidney injury in hospitalized patients.

Scientific reports
In this observational study, we used data from 59,936 hospitalized adults to construct a model. For the models constructed with all 53 variables, all five models achieved acceptable performance with the validation cohort, with the extreme gradient bo...

PlantPathoPPI: An Ensemble-based Machine Learning Architecture for Prediction of Protein-Protein Interactions between Plants and Pathogens.

Journal of molecular biology
This study aimed to develop a machine learning-based tool for predicting protein-protein interactions (PPIs) between plant-pathogen systems, addressing the challenges of experimental PPI identification. Identifying PPIs in plant-pathogen interactions...

Combining artificial intelligence assisted image segmentation and ultrasound based radiomics for the prediction of carotid plaque stability.

BMC medical imaging
PURPOSE: Utilizing artificial intelligence (AI) technology for the segmentation of plaques on ultrasound images to evaluate the stability of carotid artery plaques and analyze its diagnostic accuracy in differentiating vulnerable plaques from stable ...

A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections.

BMC pulmonary medicine
BACKGROUND: In hospitalized patients, inadequate antibiotic dosage leading to bacterial resistance and increased antimicrobial use intensity due to overexposure to antibiotics are common problems. In the present study, we constructed a machine learni...

Abnormal heart sound recognition using SVM and LSTM models in real-time mode.

Scientific reports
Cardiovascular diseases are non-communicable diseases that are considered the leading cause of death worldwide accounting for 17.9 million fatalities. Auscultation of heart sounds is the most common and valuable way of diagnosing heart diseases. Norm...

Low-cost sensor-based algal bloom labeling: a comparative study of SVM and logic methods.

Environmental monitoring and assessment
This study explores a low-cost sensor system for real-time algae bloom detection and water management. Harmful algal blooms (HABs) threaten water quality, ecosystems, and public health. Traditional detection methods, like satellite imagery and unmann...

Effectiveness of machine learning methods in detecting grooming: a systematic meta-analytic review.

Scientific reports
This study presents a systematic review (SR) and meta-analysis (MA) on the use of machine learning (ML) methods for detecting online grooming, a form of manipulation and child sexual abuse. The SR identified 33 studies from IEEE, Web of Science, Scop...

Optimizing fractionation schedules for de-escalation radiotherapy in head and neck cancers using deep reinforcement learning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: Patients with locally-advanced head and neck squamous cell carcinomas (HNSCCs), particularly those related to human papillomavirus (HPV), often achieve good locoregional control (LRC), yet they suffer significant toxicities from standard che...