Multiple machine learning algorithms were employed to construct models for assessing SLE disease activity.Results The patients were divided into two cohorts, cohort 1 used as the training set to build ...

The proposed model demonstrated the classification capabilities of the OGNB classifier with many machines learning classifiers, including Logistic regression, K-nearest neighbor, naive Bayes, Support ...

Machine learning models analyze complex patterns within medical datasets, enabling precise prediction and classification of diseases like CHD [14]- [18]. This study explores the application of three ...

Krishnaraj et al. (2023) highlight that automated loan eligibility prediction using machine learning improves efficiency, accuracy, and inclusivity, with Logistic Regression slightly outperforming ...

Timely prediction of debris flow probabilities in areas impacted by wildfires is crucial to mitigate public exposure to this hazard during post-fire rainstorms. This paper presents a machine learning ...

Keywords: machine learning, CVD, risk prediction, hypertension, traditional logistic regression Citation: Xi Y, Wang HY and Sun NL (2022) Machine learning outperforms traditional logistic regression ...