Electrocardiogram (ECG) signals are affected by different types of noise and artifacts which can hide significant information related to heart functioning. Therefore, denoising of signals is essential for accurate diagnosis of different heart diseases. This study examines and compares three different denoising techniques, namely Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Multiscale Principal Component Analysis (MSPCA), and their influence on ECG signal classification. In order to extract significant features from ECG signals, wavelet packet decomposition (WPD) technique is employed. To classify ECG signals, ensemble machine learning classifier called Rotation Forest is proposed for classification task. In this study, three different systems, namely PCA-WPD-Rotation Forest, ICA-WPD-Rotation Forest and MSPCA-WPD-Rotation Forest were analyzed and evaluated in terms of three different statistical metrics, namely F-measure, AUC value and overall accuracy. To evaluate performances of proposed systems, MIT-BIH database is used. Obtained experimental results showed that MSPCA-WPD-Rotation Forest system resulted in the highest performances when compared to PCA-WPD-Rotation Forest and ICA-WPD-Rotation Forest. Therefore, in this study following system for classification of ECG signals is proposed: MSPCA as the first component for denoising, WPD as the second component which extracts important features from ECG signals and Rotation Forest as the third component which performs classification of ECG signals. The comparison to other systems for classification of EEG signals shown that proposed system outperforms other systems on the MIT-BIH arrhythmia database under investigated conditions.

This study presents a simplified fuzzy ARTMAP (SFAM) for different classification applications. The proposed SFAM model is synergy of fuzzy logic and adaptive resonance theory (ART) neural networks. SFAM is supervised network consisting of two layers (Fuzzy ART and Inter ART) that build constant classification groups in answer to series of input patterns. Fuzzy ART layer takes a series of input patterns and relate them to output classes. Inter ART layer functions in such a way that it raises the vigilance parameter of fuzzy ART layer. By combining this two layers, SFAM is capable to perform classification very efficiently and giving very high performances. Lastly, the SFAM model is applied to different simulations. The simulation results obtained for the three different datasets: Iris, Wisconsin breast cancer and wine dataset prove that SFAM model has better performance results than other models for these classification applications.

In almost all parts of the world, breast cancer is one of the major causes of death among women. But at the same time, it is one of the most curable cancers if it is diagnosed at early stage. This paper tries to find a model that diagnoses and classifies breast cancer with high accuracy and that will help to both patients and doctors in the future. Here we present several different decision tree methods in order to classify breast cancer with high accuracy. The results achieved in this research are very promising (accuracy is 96.49 %). It is very promising result compared to previous researches where decision tree techniques were used. As benchmark test, Breast Cancer Wisconsin (Original) was used.

Data mining is information extraction from database. In this paper, we use data mining techniques to get correct medical diagnosis. In this study different techniques presented to get better accuracy using data mining tools such as Bayesian Network, Multilayer Perceptron, Decision Trees and Support Vector Machines (SVM). By using SVM, we achieved 97.72 % accuracy with WDBC dataset.