http://disciplinas.stoa.usp.br/pluginfile.php/166516/mod_resource/content/1/Radiomics.pdf

Radiomics: the process and the challenges

There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity and the quality of daily life. The treatment of these disorders is a challenging task for medical professionals. Dimensionality reduction techniques make it possible to handle big data samples, providing decision support in relation to chronic diseases. These datasets contain a series of symptoms that are used in disease prediction. The presence of redundant and irrelevant symptoms in the datasets should be identified and removed using feature selection techniques to improve classification accuracy. Therefore, the main contribution of this paper is a comparative analysis of the impact of wrapper and filter selection methods on classification performance. The filter methods that have been considered include the Correlation Feature Selection (CFS) method, the Information Gain (IG) method and the Chi-Square (CS) method. The wrapper methods that have been considered include the Best First Search (BFS) method, the Linear Forward Selection (LFS) method and the Greedy Step Wise Search (GSS) method. A Decision Tree algorithm has been used as a classifier for this analysis and is implemented through the WEKA tool. An attribute significance analysis has been performed on the diabetes, breast cancer and heart disease datasets used in the study. It was observed that the CFS method outperformed other filter methods concerning the accuracy rate and execution time. The accuracy rate using the CFS method on the datasets for heart disease, diabetes, breast cancer was 93.8%, 89.5% and 96.8% respectively. Moreover, latency delays of 1.08 s, 1.02 s and 1.01 s were noted using the same method for the respective datasets. Among wrapper methods, BFS’ performance was impressive in comparison to other methods. Maximum accuracy of 94.7%, 95.8% and 96.8% were achieved on the datasets for heart disease, diabetes and breast cancer respectively. Latency delays of 1.42 s, 1.44 s and 132 s were recorded using the same method for the respective datasets. On the basis of the obtained result, a new hybrid Attribute Evaluator method has been proposed which effectively integrates enhanced K-Means clustering with the CFS filter method and the BFS wrapper method. Furthermore, the hybrid method was evaluated with an improved decision tree classifier. The improved decision tree classifier combined clustering with classification. It was validated on 14 different chronic disease datasets and its performance was recorded. A very optimal and consistent classification performance was observed. The mean values for accuracy, specificity, sensitivity and f-score metrics were 96.7%, 96.5%, 95.6% and 96.2% respectively.

Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier

In this paper, a mathematical background has been constructed for wavelet packet based texture analysis of self similar images. The paper proposes PSD estimator based on 2-dimensional wavelet packet and compared the outcomes with Fourier transform base PSD estimator and found that 2 dimensional wavelet packet based estimator gives much insight information about the texture description as compared to the conventional 2-D FT based estimators. This paper also addresses the effectiveness of wavelet based spectrum estimation.

Wavelet Packets Based Spectral Estimation of Textured Images

The human circulatory system is a complex organ system, with multiple feedback and feedforward mechanism for regulation. When global circulatory variables are assessed, these exhibit distinct oscillatory patterns with several frequency bands with different amplitudes, probably because of the underlying complex regulation. Loss of complexity, or decomplexification, has been reported in several cardiac diseases, as well as after heart surgery. Electrocardiogram (ECG), arterial blood pressure (ABP) and laser Doppler flowmetry (LDF) of peripheral blood flow has been recorded in ten patients subjected to coronary artery bypass grafting (CABG) surgery. As such, cardiovascular oscillations have been obtained in observations pre and post surgery, divided into different classes. Heart rate variability (HRV) signals have been extracted from the ECG signals. Low frequency content in the band 0.004 2 Hz has been computed in the HRV, ABP and LDF signals with the continuous wavelet transform (CWT). Principal component analysis (PCA) has been performed of the CWT data, to investigate changes in the circulatory system due to the CABG surgery. This showed a small distinction between pre and post surgery observations. The frequency band for each signal has been further divided into six smaller subbands. Total power in each subband has been computed. This showed a statistical significantly decrease in power from before to after surgery in the HRV signals, for the five lower subbands. Also, the power loss was statistically significant in some of the subbands for the ABP signal. Further, five more features have been computed for each subband. These new features, as well as the principal component (PC) scores, for each observation, have been further analyzed with the ReliefF feature selection method. From this, the dimensionality was reduced to only contain the important features showing distinction between pre and post surgery data. Using these features, supervised classification was performed with the decision tree, discriminant analysis, K-nearest neighbor (KNN) and support vector machine (SVM) classifiers. Performance measures have been assessed with the K-fold cross validation method. Correct classification accuracy of the different classes for before and after surgery was relatively high, however performance was not perfect. This was probably due to the low number of observations, as more is data usually needed to properly train and test classifiers.

https://brage.bibsys.no/xmlui/bitstream/11250/2419098/1/14985_FULLTEXT.pdf

Machine Learning of Circulatory Oscillations in Cardiac Surgical Patients

Nowadays, cancer is one of the leading causes of death in the world. However, cancer can be treated if it is diagnosed earlier. Recently, machine learning classifiers are widely applied in cancer detection due to their accurate diagnosis in cancer classification problems. However, the performance of the classifiers can be affected by the selection of the required variables used in the classification process. To choose these variables, this research proposed two classification models using two different feature selection methods namely: Grey Relational Analysis (GRA) and Improved Grey Relational Analysis (IGRA). Both of these methods are combined with a Support Vector Machine (SVM) classifier and named as GRA-SVM and IGRA-SVM. The GRA and IGRA act as a feature selection method in the preprocessing phase of SVM classifier to recognize potential variables in cancer data that can be used as significant input to SVM classifier to improve SVM classification capability performance. Using performance measuring tools, the efficiency of the proposed classification models: GRA-SVM and IGRA-SVM based on the value of geometric mean, sensitivity, specificity, accuracy and area under Receiver Operating Characteristic curve were compared with standard SVM and other classification models from previous studies. The results showed that the proposed GRA-SVM and IGRA-SVM classification models have achieved better performance in classifying the cancer data with better results ranging between 2.64% to 88.9% in the selection of potential variables.

Abed Heshmati

Papers

ReliefF-Based Feature Selection for Automatic Tumor Classification of Mammogram Images