To date, there are no reviews on machine learning (ML) for predicting outcomes in trauma.
Consequently, it remains unclear as to how ML-based prediction models compare in the …

Abstract Machine Learning (ML) provides methods, techniques, and tools that can help
solving diagnostic and prognostic problems in a variety of medical domains. ML is being …

In this paper, we present CoLD (colorectal lesions detector) an innovative detection system
to support colorectal cancer diagnosis and detection of pre-cancerous polyps, by processing …

STUDY OBJECTIVE: Clinical and ECG data from presentation are highly discriminatory for
diagnosis of acute coronary syndromes, whereas definitive diagnosis from serial ECG and …

There are a number of different quantitative models that can be used in a medical diagnostic
decision support system (MDSS) including parametric methods (linear discriminant analysis …

Introduction: Artificial intelligence (AI) is an emerging technology with vast potential for use
in several fields of medicine. However, little is known about the application of AI in treatment …

A new approach based on the implementation of multiclass support vector machine (SVM)
with the error correcting output codes (ECOC) is presented for diagnosis of erythemato …

This paper gives an integrated view of implementing automated diagnostic systems for
clinical decision‐making. Because of the importance of making the right decision, better …

This paper presents a neural network (NN) model to evaluate an existing Health Risk
Appraisal (HRA) 2 for diabetes prediction over 3 years (1996–1998) based on a simulated …

A new approach based on an adaptive neuro‐fuzzy inference system (ANFIS) is presented
for diagnosis of diabetes diseases. The Pima Indians diabetes data set contains records of …