AIMC Topic: Support Vector Machine

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X-ray based radiomics machine learning models for predicting collapse of early-stage osteonecrosis of femoral head.

Scientific reports
This study aimed to develop an X-ray radiomics model for predicting collapse of early-stage osteonecrosis of the femoral head (ONFH). A total of 87 patients (111 hips; training set: n = 67, test set: n = 44) with non-traumatic ONFH at Association Res...

Interpretable machine learning model for prediction functional cure in chronic hepatitis B patients receiving Peg-IFN therapy: A multi-center study.

International journal of medical informatics
BACKGROUND: Functional cure is the ideal treatment goal for chronic hepatitis B (CHB) treatment. We developed and validated machine learning (ML) models to predict functional cure in CHB patients.

Explainable machine learning for predicting lung metastasis of colorectal cancer.

Scientific reports
Patients with lung metastasis of colorectal cancer typically have a poor prognosis. Therefore, establishing an effective screening and diagnosis model is paramount. Our study seeks to construct and verify a predictive model utilizing machine learning...

Integrating mass defect filtering and targeted molecular networking for foodomics research: A case study of Magnolia officinalis cortex.

Food research international (Ottawa, Ont.)
Mass spectrometry (MS)-based foodomics is widely used to tackle complex challenges in food science, although its effectiveness is often hampered by extensive data redundancy. To address this limitation, a novel MS-based foodomics strategy, integratin...

Functional connectivity anomalies in medication-naive children with ADHD: Diagnostic potential, symptoms interpretation, and a mediation model.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: To identify reliable electroencephalography (EEG) biomarkers for attention deficit/hyperactivity disorder (ADHD) by investigating anomalous functional connectivity patterns and their clinical relevance.

Comparative analysis of heart disease prediction using logistic regression, SVM, KNN, and random forest with cross-validation for improved accuracy.

Scientific reports
This primary research paper emphasizes cross-validation, where data samples are reshuffled in each iteration to form randomized subsets divided into n folds. This method improves model performance and achieves higher accuracy than the baseline model....

Fully Automated Online Adaptive Radiation Therapy Decision-Making for Cervical Cancer Using Artificial Intelligence.

International journal of radiation oncology, biology, physics
BACKGROUND: Interfraction variations during radiation therapy pose a challenge for patients with cervical cancer, highlighting the benefits of online adaptive radiation therapy (oART). However, adaptation decisions rely on subjective image reviews by...

Exploring the nexus between coastal tourism growth and eutrophication: Challenges for environmental management.

Marine pollution bulletin
Coastal tourism has witnessed rapid growth over the past two decades, often accompanied by increasing environmental concerns, particularly eutrophication in sensitive marine zones. This study explores the relationship between coastal tourism expansio...

Machine learning approaches for assessing medication transfer to human breast milk.

Journal of pharmacokinetics and pharmacodynamics
The human milk/plasma (M/P) drug concentration ratio is crucial in pharmacology, especially for breastfeeding mothers undergoing treatment. It determines the extent to which drugs ingested by the mother pass into breast milk, potentially affecting th...

Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression.

ACS nano
Optical spectroscopy, a noninvasive molecular sensing technique, offers valuable insights into material characterization, molecule identification, and biosample analysis. Despite the informativeness of high-dimensional optical spectra, their interpre...