AIMC Topic: Logistic Models

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Using Machine Learning Methods to Predict Early Treatment Outcomes for Multidrug-Resistant or Rifampicin-Resistant Tuberculosis to Enhance Patient Cure Rates: Development and Validation of Multiple Models.

Journal of medical Internet research
BACKGROUND: Early prediction of treatment outcomes for patients with multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) undergoing extended therapy is crucial for enhancing clinical prognoses and preventing the transmission of this ...

Comparative evaluation of deep learning and traditional models for predicting traffic accident severity in Saudi Arabia.

Scientific reports
Road traffic accidents are one of the leading death causes around the globe, claiming millions of lives every year. Predicting traffic accident severity is essential for road users' safety and accident prevention. Artificial neural network (ANN), Boo...

Improved American College of Surgeons NSQIP Hospital Benchmarking with Risk Adjustment for Many CPT Codes Rather Than Just the Principal Code.

Journal of the American College of Surgeons
BACKGROUND: Because of technical limitations inherent to logistic regression, NSQIP benchmarking has historically risk adjusted for procedure using only 1 principal CPT code among other predictors. This has the potential to create bias (favorable or ...

A hybrid framework of statistical, machine learning, and explainable AI methods for school dropout prediction.

PloS one
Student dropout is a significant challenge in Bangladesh, with serious impacts on both educational and socio-economic outcomes. This study investigates the factors influencing school dropout among students aged 6-24 years, employing data from the 201...

Construct prediction models for low muscle mass with metabolic syndrome using machine learning.

PloS one
BACKGROUND: Metabolic syndrome (MetS) and sarcopenia are major global public health problems, and their coexistence significantly increases the risk of death. In recent years, this trend has become increasingly prominent in younger populations, posin...

Investigating factors influencing fatalities and injuries in animal-vehicle crashes using a random parameters logit model and ensemble machine learning approaches.

PloS one
Animal-vehicle crashes (AVC) pose risks in rural areas, often leading to casualties and injuries. Despite their infrequent occurrence, AVC can have significant consequences, especially when larger animals are involved. This study investigates factors...

Predicting children and adolescents at high risk of poor health‑related quality of life using machine learning methods.

Health and quality of life outcomes
BACKGROUND: Existing research has identified health‑related quality of life (HRQoL) is influenced by a multitude of factors among children and adolescents. However, there has been relatively limited exploration of the multidimensional predictive fact...

Machine learning-based prediction model for post-stroke cerebral-cardiac syndrome: a risk stratification study.

Scientific reports
Cerebral-cardiac syndrome (CCS) is a severe cardiac complication following acute ischemic stroke, often associated with adverse outcomes. This study developed and validated a machine learning (ML) model to predict CCS using clinical, laboratory, and ...

Construction and validation of a urinary stone composition prediction model based on machine learning.

Urolithiasis
The composition of urinary calculi serves as a critical determinant for personalized surgical strategies; however, such compositional data are often unavailable preoperatively. This study aims to develop a machine learning-based preoperative predicti...