AIMC Topic: Logistic Models

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Building a predictive model to assist in the diagnosis of cervical cancer.

Future oncology (London, England)
Cervical cancer is still one of the most common gynecologic cancers in the world. Since cervical cancer is a potentially preventive cancer, earlier detection is the most effective technique for decreasing the worldwide incidence of the illness. Thi...

Predicting the Cochlear Dead Regions Using a Machine Learning-Based Approach with Oversampling Techniques.

Medicina (Kaunas, Lithuania)
: Determining the presence or absence of cochlear dead regions (DRs) is essential in clinical practice. This study proposes a machine learning (ML)-based model that applies oversampling techniques for predicting DRs in patients. : We used recursive p...

Machine learning techniques for mortality prediction in emergency departments: a systematic review.

BMJ open
OBJECTIVES: This systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments (EDs).

Using CatBoost algorithm to identify middle-aged and elderly depression, national health and nutrition examination survey 2011-2018.

Psychiatry research
Depression is one of the most common mental health problems in middle-aged and elderly people. The establishment of risk factor-based depression risk assessment model is conducive to early detection and early treatment of high-risk groups of depressi...

Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Machine learning and deep learning models are very powerful in predicting the presence of a disease. To achieve good predictions, those models require a certain amount of data to train on, whereas this amount i) is generally...

Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths.

The Journal of emergency medicine
BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, mig...

Efficient Prediction of Missed Clinical Appointment Using Machine Learning.

Computational and mathematical methods in medicine
Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pa...

Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters.

Sensors (Basel, Switzerland)
Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identific...

Prediction of all-cause mortality in coronary artery disease patients with atrial fibrillation based on machine learning models.

BMC cardiovascular disorders
BACKGROUND: Machine learning (ML) can include more diverse and more complex variables to construct models. This study aimed to develop models based on ML methods to predict the all-cause mortality in coronary artery disease (CAD) patients with atrial...