AIMC Topic: Logistic Models

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Development and validation of a novel public prediction platform for deciduous caries in preschool children: an observational study from Northwest China.

BMC pediatrics
BACKGROUND: Early childhood caries (ECC) is a major global public health concern, necessitating its early screening. This study aimed to establish a caries risk assessment (CRA) platform for managing caries in community preschool children in underdev...

Machine learning-based predictive modeling of angina pectoris in an elderly community-dwelling population: Results from the PoCOsteo study.

PloS one
BACKGROUND: Angina pectoris, a comparatively common complaint among older adults, is a critical warning sign of underlying coronary heart disease. We aimed to develop machine learning-based models using multiple algorithms to predict and identify the...

Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis.

Renal failure
INTRODUCTION: Natural language processing (NLP) has been used to analyze unstructured imaging report data, yet its application in identifying chronic kidney disease (CKD) features from kidney ultrasound reports remains unexplored.

Evaluation and analysis of risk factors for fractured vertebral recompression post-percutaneous kyphoplasty: a retrospective cohort study based on logistic regression analysis.

BMC musculoskeletal disorders
BACKGROUND: Vertebral recompression after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fractures (OVCFs) may lead to recurrent pain, deformity, and neurological impairment, compromising prognosis and quality of life.

An artificial intelligence model to predict mortality among hemodialysis patients: A retrospective validated cohort study.

Scientific reports
Hemodialysis stands as the most prevalent renal replacement therapy globally. Accurately identifying mortality among hemodialysis patients is paramount importance, as it enables the formulation of tailored interventions and facilitates timely managem...

Hysterectomy as a predictor of depression: A comprehensive analysis using logistic regression and machine learning.

Journal of affective disorders
BACKGROUND: An increasing number of studies have shown that there is an inseparable connection between hysterectomy and occurrence of depression, and the impact on patient's mental health cannot be ignored. Therefore, this study utilized the National...

Determination of milk yield in water buffaloes using multi-class logistic regression and machine learning methods.

Tropical animal health and production
In this study, Random Forest, Gradient Boosting Machines (GBM), and Support Vector Machines (SVM), Multi-Class Logistic Regression (MCLR) models were comparatively evaluated for the prediction of milk yield in water buffaloes. The study's main purpos...

Predicting Missed Appointments in Primary Care: A Personalized Machine Learning Approach.

Annals of family medicine
PURPOSE: Factors influencing missed appointments are complex and difficult to anticipate and intervene against. To optimize appointment adherence, we aimed to use personalized machine learning and big data analytics to predict the risk of and contrib...

Potential effects of endocrine-disrupting chemicals on preserved ratio impaired spirometry revealed by five different approaches.

Ecotoxicology and environmental safety
OBJECTIVE: Evidence from prior studies indicates that certain endocrine-disrupting chemicals (EDCs), such as phenols and phthalates, may serve as environmental risk factors for chronic obstructive pulmonary disease (COPD). However, no studies have ex...