The detection and classification of blast cell in Leukaemia Acute Promyelocytic Leukaemia (AML M3) blood using simulated annealing and neural networks

This paper was delivered at AIME 2011: 13th Conference on Artifical Intelligence in Medicine.This paper presents a method for the detection and classification of blast cells in M3 with others sub-types using simulated annealing and neural networks. In this paper, we increased our test result from 10 images to 20 images. We performed Hill Climbing, Simulated Annealing and Genetic Algorithms for detecting the blast cells. As a result, simulated annealing is the “best” heuristic search for detecting the leukaemia cells. From the detection, we performed features extraction on the blast cells and we classifying based on M3 and other sub-types using neural networks. We received convincing result which has targeting around 97% in classifying of M3 with other sub-types. Our results are based on real world image data from a Haematology Department.Universiti Sains Islam Malaysia and the Ministry of Higher Education, Malaysi

Artificial immune systems applications in cancer research

Artificial Immune System (AIS) is a branch of computational intelligence that has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational problems. This work focuses on the application of AIS techniques to cancer research and specifically for prediction of the recurrence of cancer in patients. The objective is to test AIS models and algorithms for cancer research by validation against actual cancer datasets. © 2011 IEEE

A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm

Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO) is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results

Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy

In this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. Its low complexity is based on the reuse of previous computations to calculate current feature relevance. The mu-TAFS algorithm --named as such to differentiate it from previous TAFS algorithms-- implements a simulated annealing technique specially designed for feature subset selection. The algorithm is applied to the maximization of gene subset relevance in several public-domain microarray data sets. The experimental results show a notoriously high classification performance and low size subsets formed by biologically meaningful genes.Postprint (published version

A Clinical Prognostic Framework for Classifying Severe Liver Disorders (SLDs) and Lungs’ Vulnerability to Virus

Most severe liver diseases (SLDs) are attributed to increased risk for cancer, and cirrhosis, through which the manifestation of fibrotic tissues and scars tends to affect liver function The role of liver is indispensable, as inner organ performing services that ranges from metabolism, immune guide, energy producer and digestive aid, just to mention a few. Prevalence of classification problem and the need for automated prognosis is the continual drive to apply data mining techniques and/or machine learning algorithms in medical diagnosis and clinical support systems. Computational scientists and researchers in the field of artificial intelligence have recorded notable efforts with existing methods/models for diagnosis or prognosis, yet their effectiveness and functional performance is not without drawback due to ambiguity of medical information and selected features in patients’ data to tell the future course. In this paper, a novel hybridized machine learning model was provided (Fuzzy c-BC) for clinical classification of Severe Liver Disorders (SLDs) and to determine Lungs Vulnerability (LV) to virus; by incorporating individual strength of fuzzy cluster means (FCM) and naive Bayes classifier (NBC) for projecting future course of every categorized liver disease (LD) and its implication to aggravate lungs infection if preventive measures are not taken in timely manner

An Entropy-based gene selection method for cancer classification using microarray data

BACKGROUND: Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of non-redundant but relevant genes is difficult. The selected gene set should be small enough to allow diagnosis even in regular clinical laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Here an entropy-based method is proposed that selects genes related to the different cancer classes while at the same time reducing the redundancy among the genes. RESULTS: The present study identifies a subset of features by maximizing the relevance and minimizing the redundancy of the selected genes. A merit called normalized mutual information is employed to measure the relevance and the redundancy of the genes. In order to find a more representative subset of features, an iterative procedure is adopted that incorporates an initial clustering followed by data partitioning and the application of the algorithm to each of the partitions. A leave-one-out approach then selects the most commonly selected genes across all the different runs and the gene selection algorithm is applied again to pare down the list of selected genes until a minimal subset is obtained that gives a satisfactory accuracy of classification. The algorithm was applied to three different data sets and the results obtained were compared to work done by others using the same data sets CONCLUSION: This study presents an entropy-based iterative algorithm for selecting genes from microarray data that are able to classify various cancer sub-types with high accuracy. In addition, the feature set obtained is very compact, that is, the redundancy between genes is reduced to a large extent. This implies that classifiers can be built with a smaller subset of genes

Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.Comment: 35 page

An approach based on tunicate swarm algorithm to solve partitional clustering problem

The tunicate swarm algorithm (TSA) is a newly proposed population-based swarm optimizer for solving global optimization problems. TSA uses best solution in the population in order improve the intensification and diversification of the tunicates. Thus, the possibility of finding a better position for search agents has increased. The aim of the clustering algorithms is to distributed the data instances into some groups according to similar and dissimilar features of instances. Therefore, with a proper clustering algorithm the dataset will be separated to some groups and it’s expected that the similarities of groups will be minimum. In this work, firstly, an approach based on TSA has proposed for solving partitional clustering problem. Then, the TSA is implemented on ten different clustering problems taken from UCI Machine Learning Repository, and the clustering performance of the TSA is compared with the performances of the three well known clustering algorithms such as fuzzy c-means, k-means and k-medoids. The experimental results and comparisons show that the TSA based approach is highly competitive and robust optimizer for solving the partitional clustering problems