Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases
Pattern Recognition Letters
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Diagnosis of valvular heart disease through neural networks ensembles
Computer Methods and Programs in Biomedicine
An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Hybrid intelligent techniques for MRI brain images classification
Digital Signal Processing
Evaluation of ensemble methods for diagnosing of valvular heart disease
Expert Systems with Applications: An International Journal
PCA plus LDA on wavelet co-occurrence histogram features: application to CBIR
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
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In the last two decades, the use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems have improved a great deal to help the medical experts in diagnosing. In this work, we investigate the use of principal component analysis (PCA), artificial immune system (AIS) and fuzzy k-NN to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white de-noising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, wavelet packet decomposition was used. As a next step, wavelet entropy was considered as features. For reducing the complexity of the system, PCA was used for feature reduction. In the classification stage, AIS and fuzzy k-NN were used. To evaluate the performance of the proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters; 95.9% sensitivity and 96% specificity rate was obtained.