Brahim Belhaouari Samir

Systematic Analysis of Applied Data Mining Based Optimization Algorithms in Clinical Attribute Extraction and Classification for Diagnosis of Cardiac Patients

This chapter covers the data mining techniques applied to the processing of clinical data to detect cardiovascular diseases. Technology evaluation and rapid development in medical diagnosis have always attracted the researchers to deliver novelty. Chronic diseases such as cancer and cardiac have been under discussion to ease their treatments using computer aided diagnosis (CAD) by optimizing their architectural complexities with better accuracy rate. To design a medical diagnostic system, raw ECG Signals, clinical and laboratory results are utilized to proceed further processing and classification. The significance of an optimized system is to give timely detection with lesser but essential clinical attributes for a patient to ensue surgical or medical follow-up. Such appropriate diagnostic systems which can detect abnormalities in clinical data and signals are truly vital and various soft computing techniques based on data mining have been applied. Hybrid approaches derived from data mining algorithms are immensely incorporated for extraction and classification of clinical records to eliminate possible redundancy and missing details which can cause worse overhead issues for the designed systems. It also extends its applications in selection, processing and ranking clinical attributes which are integral components of any medical diagnostic system. Such systems are evaluated by determining the performance measures such as system’s accuracy, sensitivity and specificity. Various supervised and unsupervised learning algorithms have been ensemble with feature processing methods to optimize in the best possible manner to detect cardiac abnormalities. This chapter analyzes all the earlier applied approaches for the cardiac disease and highlights the associated inadequacies. It also includes the architectural constraints of developing classification models. Hybrid methodologies combined with requisite clinical extraction and ranking tools to enhance system’s efficiency are also discussed. This systematic analysis of recent applied approaches for cardiac disease, aids in the domain of clinical data processing to discuss the present limitations and overcome the forthcoming complexity issues in terms of time and memory. Further, it explains that how efficient techniques for data processing and classification have not been used appropriately by considering their strengths in either phase, which leads to processing overhead and increased false alarms. Overall, the aim of this chapter is to resolve assorted concerns and challenges for designing optimized cardiac diagnostic systems with well tuned architecture.

Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method

This paper introduces a new method of feature extraction from Wavelet coefficients for classification of digital mammograms. A matrix is constructed by putting Wavelet coefficients of each image of a building set as a row vector. The method consists then on selecting by threshold, the columns which will maximize the Euclidian distances between the different class representatives. The selected columns are then used as features for classification. The method is tested using a set of images provided by the Mammographic Image Analysis Society (MIAS) to classify between normal and abnormal and then between benign and malignant tissues. For both classifications, a high accuracy rate (98%) is achieved.

Data Mining of Protein Sequences with Amino Acid Position-Based Feature Encoding Technique

Biological data mining has been emerging as a new area of research by incorporating artificial intelligence and biology techniques for automatic analysis of biological sequence data. The size of the biological data collected under the Human Genome Project is growing exponentially. The available data is comprised of DNA, RNA and protein sequences. Automatic classification of protein sequences into different groups might be utilized to infer the structure, function and evolutionary information of an unknown protein sequence. The accurate classification of protein sequences into family/superfamily based on the primary sequence is a very complex and open problem. In this paper, an amino acid position-based feature encoding technique is proposed to represent a protein sequence using a fixed length numeric feature vector. The classification results indicate that the proposed encoding technique with a decision tree classification algorithm has achieved 85.9 % classification accuracy over the Yeast protein sequence dataset.

A Computer Aided Diagnosis System for Lung Cancer based on Statistical and Machine Learning Techniques

lung Cancer is believed to be among the primary factors for death across the world. Within this paper, statistical and machine learning techniques are employed to build a computer aided diagnosis system for the purpose of classifying lung cancer. The system includes preprocessing phase, feature extraction phase, feature selection phase and classification phase. For feature extraction, wavelet transform is used and for feature selection, two-step statistical techniques are applied. Clustering-K-nearest-neighbor classifier is employed for classification. The Japanese Society of Radiological Technology’s standard dataset of lung cancer has been utilized to evaluate the system. The dataset has 154 nodule regions (abnormal) - where 100 are malignant and 54 are benign - and 92 non-nodule regions (normal). An Accuracy of 99.15% and 98.70 % for classification have been achieved for normal versus abnormal and benign versus malignant respectively, this substantiate the capabilities of the approach presented in this paper.

On the combination of wavelet and curvelet for feature extraction to classify lung cancer on chest radiographs

This paper investigates the combination of multiresolution methods for feature extraction for lung cancer. The focus is on the impact of combining wavelet and curvelet on the accuracy of the disease diagnosis. The paper investigates feature extraction with two different levels of wavelet, two different wavelet functions and the combination of wavelet and curvelet to obtain a high classification rate. The findings suggest the potential of combining different multiresolution methods in achieving high accuracy rates.

Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm

growth of intelligent intrusion and diverse attack techniques in network systems stimulate computer scientists and mathematical researchers to challenge the dangers of intelligent attacks. In this work, we integrate artificial immune algorithm with non-linear classification of pattern recognition and machine learning methods to solve the problem of intrusion detection in network systems. A new non classification algorithm was developed based on the danger theory model of human immune system (HIS).The abstract model of system algorithm is inspired from HIS cell mechanism mainly, the Dendritic cell behavior and T-cell mechanisms. Classification techniques using k-nearest neighbor (k-NN) or Gaussian Mixture (GMM) almost have the common sense that they believe the neighboring data. The algorithm tested use KDD Cup dataset and the result shows a significant improvement in detection accuracy and reducing the false alerts.

Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm

Machine learning is being implemented in bioinformatics and computational biology to solve challenging problems emerged in the analysis and modeling of biological data such as DNA, RNA, and protein. The major problems in classifying protein sequences into existing families/superfamilies are the following: the selection of a suitable sequence encoding method, the extraction of an optimized subset of features that possesses significant discriminatory information, and the adaptation of an appropriate learning algorithm that classifies protein sequences with higher classification accuracy. The accurate classification of protein sequence would be helpful in determining the structure and function of novel protein sequences. In this article, we have proposed a distance‐based sequence encoding algorithm that captures the sequences statistical characteristics along with amino acids sequence order information. A statistical metric‐based feature selection algorithm is then adopted to identify the reduced set of features to represent the original feature space. The performance of the proposed technique is validated using some of the best performing classifiers implemented previously for protein sequence classification. An average classification accuracy of 92% was achieved on the yeast protein sequence data set downloaded from the benchmark UniProtKB database.

A novel approach for segment level audio retrieval using singular value decomposition

Audio files with different file names may contain same data in it. This duplicity of data occupies the system storage without providing any benefit to the user. It is obligatory to eliminate this duplicity, to optimize the system storage efficiently. To solve this problem, a segment level audio retrieval system is presented in this paper. The system is used to retrieve the duplicate audio files from the database using audio retrieval technique. The segments of the audio files are given as a query to the system. These segments are the audio pieces range between 1 to 2 seconds. Use of segments is preferred over original file because of their smile sizes, to reduce the system complexity. Using the system, duplicity of two files is decided by the similarity between their feature matrices. The system is purely application driven and does not depend on the particular audio characteristics. Experiments are done on the large audio databases and system is evaluated accordingly. The system is provided high retrieval accuracy for all the experiments.

Classification of GPCRs proteins using a statistical encoding method

Classification of G protein-coupled receptors (GPCRs) according to their functions is an ongoing area of research which is helpful for the pharmaceutical industry in the development of drug targets for major diseases. Currently, more than 40% drugs in the market target GPCRs. The experimental methods of determining their function are very expensive and time consuming. Due to a rapid and constant increase in the GPCRs proteins in the public databases, it is extremely important to develop computational techniques that lessen the gap between the sequenced proteins and proteins with known functions. In this paper, a statistical method was utilized to encode proteins sequences. The encoding technique considers various distances for an amino acid in a sequence at different levels of decompositions. The Neural Network and Support Vector Machines classifiers were compared on 2 well-known GPCRs datasets. The results showed that better performance is achieved using neural network classifier. The classification accuracies were in the range of 94 to 98%.

Species Identification Using Part of DNA Sequence: Evidence from Machine Learning Algorithms

In biological studies, species identification is considered one of the most important issues. Several methods have been suggested to identify species using the whole DNA sequences. In this study, we present new insights for species identification using only part of the DNA sequence. The Clustering k-Nearest Neighbor (K-C-NN) and Support Vector Machine (SVM) classifiers were used to test and evaluate the improved statistical features extracted from DNA sequences for four species (Aquifex aeolicus, Bacillus subtilis, Aeropyrum pernix and Buchnera sp). The results show that part of DNA sequences can be used to identify species.