AIMC Topic: ROC Curve

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Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease.

International journal of neural systems
Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the constr...

The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM.

Computational and mathematical methods in medicine
Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan. However, the quality of the training set may reduce because of mixing with the wrong docking poses and it will affect the screening efficiencies. To...

Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection.

Medical & biological engineering & computing
Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation ...

Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer.

Artificial intelligence in medicine
OBJECTIVE: Machine learning techniques can be used to extract predictive models for diseases from electronic medical records (EMRs). However, the nature of EMRs makes it difficult to apply off-the-shelf machine learning techniques while still exploit...

A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM.

Journal of medical systems
The use of machine learning tools has become widespread in medical diagnosis. The main reason for this is the effective results obtained from classification and diagnosis systems developed to help medical professionals in the diagnosis phase of disea...

Genome-enabled prediction using probabilistic neural network classifiers.

BMC genomics
BACKGROUND: Multi-layer perceptron (MLP) and radial basis function neural networks (RBFNN) have been shown to be effective in genome-enabled prediction. Here, we evaluated and compared the classification performance of an MLP classifier versus that o...

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

IEEE transactions on medical imaging
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that ha...

Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier.

Journal of medical systems
In this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to decompose ECG signals into different successive frequency bands. A set of ...

Symmetry Analysis of Gait between Left and Right Limb Using Cross-Fuzzy Entropy.

Computational and mathematical methods in medicine
The purpose of this paper is the investigation of gait symmetry problem by using cross-fuzzy entropy (C-FuzzyEn), which is a recently proposed cross entropy that has many merits as compared to the frequently used cross sample entropy (C-SampleEn). Fi...

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
OBJECTIVES: Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns wit...