AIMC Topic: ROC Curve

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Assessment and prediction models for the quantitative and qualitative reserve of the ovary using machine learning.

Journal of ovarian research
BACKGROUND: The age-related decline of fertility is caused by a reduction of the ovarian reserve, which is represented by the number and quality of oocytes in the ovaries. Anti-Müllerian hormone (AMH) is considered one of the most useful markers of t...

Establishment of an interpretable MRI radiomics-based machine learning model capable of predicting axillary lymph node metastasis in invasive breast cancer.

Scientific reports
This study sought to develop a radiomics model capable of predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer (IBC) based on dual-sequence magnetic resonance imaging(MRI) of diffusion-weighted imaging (DWI) and dy...

Explainable machine learning-driven models for predicting Parkinson's disease and its prognosis: obesity patterns associations and models development using NHANES 1999-2018 data.

Lipids in health and disease
BACKGROUND: Parkinson's disease (PD) is a prevalent neurodegenerative condition, the effect of obesity on PD remains controversial. We aimed to investigate the associations of obesity patterns on PD and all-cause mortality, while developing machine l...

Task based evaluation of sparse view CT reconstruction techniques for intracranial hemorrhage diagnosis using an AI observer model.

Scientific reports
Sparse-view computed tomography (CT) holds promise for reducing radiation exposure and enabling novel system designs. Traditional reconstruction algorithms, including Filtered Backprojection (FBP) and Model-Based Iterative Reconstruction (MBIR), ofte...

Beyond traditional models: Jaya-optimized ensembles for accurate heart disease prediction.

Computers in biology and medicine
INTRODUCTION: Heart Disease (HD) stands as the foremost reason for mortality all over the world for both men and women. Millions of people are affected worldwide every year, resulting in numerous fatalities. Timely and precise detection is essential ...

Machine learning to evaluate the effects of non-clinical social determinant features in predicting colorectal Cancer mortality in a medically underserved Appalachian population.

Scientific reports
Colorectal cancer (CRC) is the 2nd leading cause of cancer death in the United States (US). Rural Appalachia suffers the highest CRC incidence and mortality rates. There are several non-clinical health-related social determinant factors (SDOH) associ...

Mapping burnt areas using very high-resolution imagery and deep learning algorithms - a case study in Bandipur, India.

PloS one
Burnt area (BA) mapping is crucial for assessing wildfire impact, guiding restoration efforts, and improving fire management strategies. Accurate BA data helps estimate carbon emissions, biodiversity loss, and land surface properties post-fire change...

Plasma Metabolomics and Machine Learning Reveals Metabolic Alterations and Diagnostic Biomarkers for Deep Venous Thrombosis in Hypertensive Patients after Traumatic Fracture.

Journal of proteome research
We aimed to explore the metabolic dysregulations and diagnostic biomarkers for post-traumatic deep venous thrombosis (pt-DVT) in hypertensive (HPT) patients after fracture. An untargeted ultraperformance liquid chromatography-mass spectrometry-based ...

Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients.

Scientific reports
Following complete mesocolic excision (CME), heart failure (HF) emerges as a significant complication, exerting substantial impacts on both short-term and long-term patient prognoses. The primary objective of our investigation was to develop a machin...