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Logistic Models

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Logistic regression analysis and machine learning for predicting post-stroke gait independence: a retrospective study.

Scientific reports
This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We dev...

Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study.

BMC medical research methodology
BACKGROUND: The prognosis, recurrence rates, and secondary prevention strategies varied significantly among different subtypes of acute ischemic stroke (AIS). Machine learning (ML) techniques can uncover intricate, non-linear relationships within med...

Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data.

Archives of osteoporosis
UNLABELLED: Automated screening for vertebral fractures could improve outcomes. We achieved an AUC-ROC = 0.968 for the prediction of moderate to severe fracture using a GAM with age and three maximal vertebral body scores of fracture from a convoluti...

Handling missing data and measurement error for early-onset myopia risk prediction models.

BMC medical research methodology
BACKGROUND: Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction m...

Use machine learning models to identify and assess risk factors for coronary artery disease.

PloS one
Accurate prediction of coronary artery disease (CAD) is crucial for enabling early clinical diagnosis and tailoring personalized treatment options. This study attempts to construct a machine learning (ML) model for predicting CAD risk and further elu...

Predicting Mortality in Sepsis-Associated Acute Respiratory Distress Syndrome: A Machine Learning Approach Using the MIMIC-III Database.

Journal of intensive care medicine
BackgroundTo develop and validate a mortality prediction model for patients with sepsis-associated Acute Respiratory Distress Syndrome (ARDS).MethodsThis retrospective cohort study included 2466 patients diagnosed with sepsis and ARDS within 24 h of ...

Machine learning techniques to identify risk factors of breast cancer among women in Mashhad, Iran.

Journal of preventive medicine and hygiene
BACKGROUND: Low survival rates of breast cancer in developing countries are mainly due to the lack of early detection plans and adequate diagnosis and treatment facilities.

Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data.

Journal of medical Internet research
BACKGROUND: Small clinics are important in providing health care in local communities. Accurately predicting their closure would help manage health care resource allocation. There have been few studies on the prediction of clinic closure using machin...

Unveiling the potential of machine learning approaches in predicting the emergence of stroke at its onset: a predicting framework.

Scientific reports
A stroke is a dangerous, life-threatening disease that mostly affects people over 65, but an unhealthy diet is also contributing to the development of strokes at younger ages. Strokes can be treated successfully if they are identified early enough, a...