AIMC Topic: Models, Statistical

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Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models.

Scientific reports
Accurate forecasting of diabetes burden is essential for healthcare planning, resource allocation, and policy-making. While deep learning models have demonstrated superior predictive capabilities, their real-world applicability is constrained by comp...

Statistical variability in comparing accuracy of neuroimaging based classification models via cross validation.

Scientific reports
Machine learning (ML) has significantly transformed biomedical research, leading to a growing interest in model development to advance classification accuracy in various clinical applications. However, this progress raises essential questions regardi...

Primary prevention cardiovascular disease risk prediction model for contemporary Chinese (1°P-CARDIAC): Model derivation and validation using a hybrid statistical and machine-learning approach.

PloS one
BACKGROUND: Cardiovascular disease (CVD) is the leading cause of mortality and morbidity in China and worldwide while we are lacking in validated primary prevention model specifically for Chinese. To identify CVD high-risk individuals for early inter...

Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis.

Annals of medicine
INTRODUCTION: Accurate breast cancer risk prediction is essential for early detection and personalized prevention strategies. While traditional models, such as Gail and Tyrer-Cuzick, are widely utilized, machine learning-based approaches may offer en...

Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.

BMC medical research methodology
BACKGROUND: Individualized prediction of health outcomes supports clinical medicine and decision making. Our primary objective was to offer a comprehensive survey of methods for the dynamic prediction of Alzheimer's disease (AD), encompassing both co...

Statistical and machine learning models for predicting university dropout and scholarship impact.

PloS one
Although student dropout is an inevitable aspect of university enrollment, when analyzed, universities can gather information which enables them to take preventative actions that mitigate dropout risk. We study a data set consisting of 4,424 records ...

MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.

Physics in medicine and biology
Magnetic resonance imaging (MRI) is essential in clinical and research contexts, providing exceptional soft-tissue contrast. However, prolonged acquisition times often lead to patient discomfort and motion artifacts. Diffusion-based deep learning sup...

A comparative study of various statistical and machine learning models for predicting restaurant demand in Bangladesh.

PloS one
Precise demand forecasting has become crucial for merchants due to the growing complexity of client behavior and market dynamics. This allows them to enhance inventory management, minimize instances of stock outs, and enhance overall operational effi...

Enhancing particulate matter prediction in Delhi: insights from statistical and machine learning models.

Environmental monitoring and assessment
This study advances our approach to modeling particulate matter levels-specifically, PM and PM-in Delhi's dynamic urban environment through an extensive evaluation of traditional time series models (ARIMAX, SARIMAX) and machine learning models (RF, S...