AIMC Topic: Machine Learning

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Efficient diagnosis of diabetes mellitus using an improved ensemble method.

Scientific reports
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification a...

Identification of Novel Biomarkers for Ischemic Stroke Through Integrated Bioinformatics Analysis and Machine Learning.

Journal of molecular neuroscience : MN
Ischemic stroke leads to permanent damage to the affected brain tissue, with strict time constraints for effective treatment. Predictive biomarkers demonstrate great potential in the clinical diagnosis of ischemic stroke, significantly enhancing the ...

Detecting noncredible symptomology in ADHD evaluations using machine learning.

Journal of clinical and experimental neuropsychology
INTRODUCTION: Diagnostic evaluations for attention-deficit/hyperactivity disorder (ADHD) are becoming increasingly complicated by the number of adults who fabricate or exaggerate symptoms. Novel methods are needed to improve the assessment process re...

Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management.

Journal of clinical research in pediatric endocrinology
OBJECTIVE: The honeymoon phase in type 1 diabetes (T1D) represents a temporary improvement in glycemic control but may complicate insulin management. The aim was to develop and validate a machine learning (ML)-driven method for accurately detecting t...

Machine-learning model based on ultrasomics for non-invasive evaluation of fibrosis in IgA nephropathy.

European radiology
OBJECTIVES: To develop and validate an ultrasomics-based machine-learning (ML) model for non-invasive assessment of interstitial fibrosis and tubular atrophy (IF/TA) in patients with IgA nephropathy (IgAN).

A radiomics model combining machine learning and neural networks for high-accuracy prediction of cervical lymph node metastasis on ultrasound of head and neck squamous cell carcinoma.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: This study aimed to develop an ultrasound image-based radiomics model for diagnosing cervical lymph node (LN) metastasis in patients with head and neck squamous cell carcinoma (HNSCC) that shows higher accuracy than previous models.

Machine learning models for preventative mobile health asthma control.

The Journal of asthma : official journal of the Association for the Care of Asthma
INTRODUCTION: Asthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technolog...

Machine Learning to Predict Mortality in Older Patients With Cancer: Development and External Validation of the Geriatric Cancer Scoring System Using Two Large French Cohorts.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: Establishing an accurate prognosis remains challenging in older patients with cancer because of the population's heterogeneity and the current predictive models' reduced ability to capture the complex interactions between oncologic and geria...

Artificial intelligence-enabled obesity prediction: A systematic review of cohort data analysis.

International journal of medical informatics
BACKGROUND: Obesity, now the fifth leading global cause of death, has seen a dramatic rise in prevalence over the last forty years. It significantly increases the risk of diseases such as type 2 diabetes and cardiovascular disease. Early identificati...

Machine learning-based algorithm of drug-resistant prediction in newly diagnosed patients with temporal lobe epilepsy.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVES: To develop a predicted algorithm for drug-resistant epilepsy (DRE) in newly diagnosed temporal lobe epilepsy (TLE) patients.