Neural Networks
Neural network learning and expert systems
Neural network learning and expert systems
Induction of one-level decision trees
ML92 Proceedings of the ninth international workshop on Machine learning
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
An introduction to computational learning theory
An introduction to computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Practical Applications of Fuzzy Technologies
Practical Applications of Fuzzy Technologies
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Knowledge-Based Systems
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Compressed sampling for heart rate monitoring
Computer Methods and Programs in Biomedicine
Computer-aided diagnosis of diabetic retinopathy: A review
Computers in Biology and Medicine
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Diabetes mellitus (DM) affects considerable number of people in the world and the number of cases is increasing every year. Due to a strong link to the genetic basis of the disease, it is extremely difficult to cure. However, it can be controlled to prevent severe consequences, such as organ damage. Therefore, diabetes diagnosis and monitoring of its treatment is very important. In this paper, we have proposed a non-invasive diagnosis support system for DM. The system determines whether or not diabetes is present by determining the cardiac health of a patient using heart rate variability (HRV) analysis. This analysis was based on nine nonlinear features namely: Approximate Entropy (ApEn), largest Lyapunov exponet (LLE), detrended fluctuation analysis (DFA) and recurrence quantification analysis (RQA). Clinically significant measures were used as input to classification algorithms, namely AdaBoost, decision tree (DT), fuzzy Sugeno classifier (FSC), k-nearest neighbor algorithm (k-NN), probabilistic neural network (PNN) and support vector machine (SVM). Ten-fold stratified cross-validation was used to select the best classifier. AdaBoost, with least squares (LS) as weak learner, performed better than the other classifiers, yielding an average accuracy of 90%, sensitivity of 92.5% and specificity of 88.7%.