Risk factors and machine learning model for predicting ...

Identification of Risk Factors and Machine Learning-Based ... - MDPI

The aim of this paper is: (i) To provide a robust feature selection (FS) approach that could identify important risk factors which contribute to the prediction ...

Machine Learning to predict risk of items [closed]

Some of these features will have an effect on your models, others will not. But it should be your goal to start with a long list of factors and ...

Building Risk Prediction Models for Type 2 Diabetes Using Machine ...

Of the 8 predictive models, the neural network model gave the best model performance with the highest AUC value; however, the decision tree ...

A Machine Learning Model for Predicting Individual Substance ...

To identify the key risk-factors of individual substance abuse, we extract association rules from the significant features and subsequent ...

Prediction and analysis of risk factors for diabetic retinopathy based ...

In this study, a diabetic retinopathy risk prediction model integrating machine learning models and SHAP was established to increase the accuracy of risk ...

Machine learning algorithms to predict risk factors for POD | CIA

In recent years, artificial intelligence (AI) has been developing rapidly in the medical field. Machine learning, as a major branch of AI, has ...

Machine learning models rank predictive risks for Alzheimer's disease

“We all know Alzheimer's disease is a later-onset disease, so we know age is an important risk factor. But when we consider risk only for people ...

Identifying risk factors in a study - Cross Validated - Stack Exchange

First, to the distinction you're making between "predict the outcome class" and "know the risk factors," the only way to get to the latter is ...

(PDF) Predicting the Risk of Hypertension Based on Several Easy-to ...

... Initial risk prediction models relied on cross-sectional and longitudinal data. Over time, supervised ML algorithms, and clinical and ...

Can machine-learning improve cardiovascular risk prediction using ...

Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine- ...

Unraveling complex relationships between COVID-19 risk factors ...

Our purpose was to develop machine learning (ML)-based models for the prediction of mortality and severity among patients with COVID-19.

How Machine Learning is Transforming Disease Risk Prediction in ...

Machine learning models can be developed to enhance risk prediction beyond traditional risk factors. For example, these models can include ...

A machine learning model for disease risk prediction by integrating ...

A machine learning model for disease risk prediction by integrating genetic and non-genetic factors. Abstract: Polygenic risk score (PRS) has been widely used ...

Utilization of machine-learning models to accurately predict the risk ...

In the context of risk stratification, ML models generate better performance than traditional prediction models [23, 24], owing to their ability ...

longitudinal cohort study using cardiovascular disease as exemplar

The logistic models and commonly used machine learning models should not be directly applied to the prediction of long term risks without ...

empirical assessment of model stab. in ML - YouTube

ID 130: Why predicting risk can't identify risk factors: empirical assessment of model stab. in ML · Machine Learning for Healthcare · ID 131: Few ...

Risk factor determinants and comparison of supervised machine ...

In recent days, machine learning algorithm has shown a potential application in the area of disease prediction, which has recently gained.

Machine learning models for predicting critical illness risk in ...

Machine learning is broadly defined as a body of computational methods/models that use patterns in data to improve performance or make accurate predictions (9).

Factors influencing clinician and patient interaction with machine ...

Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is ...

Risk Factor Analysis Based on Deep Learning Models

The abstract features obtained by deep learning methods can represent the essentials of raw inputs, and give a good prediction performance in disease diagnosis.