Classification Of Medical Datasets Research Articles

A Hybrid Feature Selection Method for Improve the Accuracy of Medical Classification Process

Generally, medical dataset classification has become one of the biggest problems in data mining research. Every database has a given number of features but it is observed that some of these features can be redundant and can be harmful as well as disrupt the process of classification and this problem is known as a high dimensionality problem. Dimensionality reduction in data preprocessing is critical for increasing the performance of machine learning algorithms. Besides the contribution of feature subset selection in dimensionality reduction gives a significant improvement in classification accuracy. In this paper, we proposed a new hybrid feature selection approach based on (GA assisted by KNN) to deal with issues of high dimensionality in biomedical data classification. The proposed method first applies the combination between GA and KNN for feature selection to find the optimal subset of features where the classification accuracy of the k-Nearest Neighbor (kNN) method is used as the fitness function for GA. After selecting the best-suggested subset of features, Support Vector Machine (SVM) are used as the classifiers. The proposed method experiments on five medical datasets of the UCI Machine Learning Repository. It is noted that the suggested technique performs admirably on these databases, achieving higher classification accuracy while using fewer features.

Neuronal Communication Genetic Algorithm-Based Inductive Learning

The development of powerful learning strategies in the medical domain constitutes a real challenge. Machine learning algorithms are used to extract high-level knowledge from medical datasets. Rule-based machine learning algorithms are easily interpreted by humans. To build a robust rule-based algorithm, a new hybrid metaheuristic was proposed for the classification of medical datasets. The hybrid approach uses neural communication and genetic algorithm-based inductive learning to build a robust model for disease prediction. The resulting classification models are characterized by good predictive accuracy and relatively small size. The results on 16 well-known medical datasets from the UCI machine learning repository shows the efficiency of the proposed approach compared to other states-of-the-art approaches.

Classification of Medical Datasets using a Modified Adaptive Fuzzy Inference System

Classification of Medical Datasets using a Modified Adaptive Fuzzy Inference System

Density Based Feature Selection Method for Medical Datasets

High dimensional data are found in the medical domain that needs to be processed for improved data analysis. In order to deal with the curse of dimensionality, feature selection process is employed in almost all data mining applications. In this research work, Density based Feature Selection (DFS) method that ranks the features by finding the Probability Density Function (PDF) of each feature is applied to medical datasets that suffer from the curse of dimensionality. The DFS method is a filter based approach that selects the most discriminatory features from the given feature set. The feature selection method evaluates the importance of the feature with regard to the target class using density function. The DFS method has major advantages over other methods, since it is based on the ranking method to select the most discriminatory features from the whole feature set. This research work finds the best feature subset that can be used in prediction and classification of medical datasets imbibed with high dimensionality. The DFS method based on PDF is applied on the three medical datasets namely Chronic Kidney Disease (CKD) dataset, Breast Cancer Wisconsin Dataset and Parkinsons Dataset. The proposed feature selection method evaluates the merit of each feature, assign weights to the feature and rank the features based on their feature density. The reduced feature subset is then validated by the application three classification algorithms namely Support Vector Machine (SVM), Gradient Boosting, and Convolutional Neural Network (CNN). The performance of the classification algorithms are evaluated based on the performance metrics Accuracy, Sensitivity and Specificity. Experimental results indicate that the performance of the classification algorithms SVM, Gradient Boosting, and CNN is improved after the feature selection process.

Retracted: Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier.

[This retracts the article DOI: 10.1155/2015/418060.].

Rough – Granular Computing knowledge discovery models for medical classification

Medical domain has become one of the most important areas of research in order to richness huge amounts of medical information about the symptoms of diseases and how to distinguish between them to diagnose it correctly. Knowledge discovery models play vital role in refinement and mining of medical indicators to help medical experts to settle treatment decisions. This paper introduces four hybrid Rough – Granular Computing knowledge discovery models based on Rough Sets Theory, Artificial Neural Networks, Genetic Algorithm and Rough Mereology Theory. A comparative analysis of various knowledge discovery models that use different knowledge discovery techniques for data pre-processing, reduction, and data mining supports medical experts to extract the main medical indicators, to reduce the misdiagnosis rates and to improve decision-making for medical diagnosis and treatment. The proposed models utilized two medical datasets: Coronary Heart Disease dataset and Hepatitis C Virus dataset. The main purpose of this paper was to explore and evaluate the proposed models based on Granular Computing methodology for knowledge extraction according to different evaluation criteria for classification of medical datasets. Another purpose is to make enhancement in the frame of KDD processes for supervised learning using Granular Computing methodology.

Hybrid data mining model for the classification and prediction of medical datasets

Prediction and classification of medical datasets help in reducing the number of diagnostic procedure to identify the diseases there by providing economical solutions for healthcare systems and medical diagnosis software system. Data pre-processing plays a vital role in preparing effective and efficient data for data mining. Feature selection helps in providing important attributes for building comprehensive predictive models. In many existing work on classification and prediction of medical datasets, either full set of attributes or reduced set of attributes are used to find the classification accuracy. But the classification accuracy achieved from these is not satisfactory. Hence, in this paper we propose hybrid data mining predictive model for attribute selection based on entropy evaluation, mean evaluation, and threshold evaluation. In this prediction model we use feature subset selection methods (FSM) like forward selection and backward elimination method to select the best subset of attributes to reduce training time and to improve performance of the system.

An Algorithm for Clustering and Classification of Medical Datasets Using k-Means and Radial Basis Function Neural Networks

Classification and clustering is an integral part of any pattern recognition and data mining system. These methods allow the using huge size of databases is a challenging issue for machine learning and data sciences. This paper presents a hybrid approach by adopting classification via clustering with simple k-means algorithm with Euclidean distance function and radial basis function network for classification. The proposed approach conventionally used in medical datasets such as diabetes diagnostics, hepatitis and liver diseases these are commonly detected and reduce the quality of human life. These medical datasets such as liver diseases, hepatitis and diabetes datasets were obtained from University of California, Irvine (UCI) data repositories and to test the classifier accuracies with other popular methods on these datasets. Experiment results show that the proposed method performance is improved classifier accuracy and robustness for data mining and machine learning applications.

Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier.

Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

Feature Selection on Classification of Medical Datasets based on Particle Swarm Optimization

lassification analysis is widely adopted for healthcare applications to support medical diagnostic decisions, improving quality of patient care, etc. A subset dataset of the extensive amounts of data stored in medical databases is selected for training. If the training dataset contains irrelevant features, classification analysis may produce less accurate and less understandable results. Feature subset selection is one of data preprocessing step, which is of immense importance in the field of data mining. This paper proposes the filter and wrapper approaches with Particle Swarm Optimization (PSO) as a feature selection methods for medical data. The performance of the proposed methods is compared with another feature selection algorithm based on Genetic approach. The two algorithms are applied to three medical data sets The results show that the feature subset recognized by the proposed PSO when given as input to five classifiers, namely decision tree, Naive Bayes, Bayesian, Radial basis function and k-nearest neighbor classifiers showed enhanced classification accuracy over all given types of classification methods.

HColonies: a new hybrid metaheuristic for medical data classification

Medical data feature a number of characteristics that make their classification a complex task. Yet, the societal significance of the subject and the computational challenge it presents has caused the classification of medical datasets to be a popular research area. A new hybrid metaheuristic is presented for the classification task of medical datasets. The hybrid ant–bee colonies (HColonies) consists of two phases: an ant colony optimization (ACO) phase and an artificial bee colony (ABC) phase. The food sources of ABC are initialized into decision lists, constructed during the ACO phase using different subsets of the training data. The task of the ABC is to optimize the obtained decision lists. New variants of the ABC operators are proposed to suit the classification task. Results on a number of benchmark, real-world medical datasets show the usefulness of the proposed approach. Classification models obtained feature good predictive accuracy and relatively small model size.

Hybrid System based on Rough Sets and Genetic Algorithms for Medical Data Classifications

Computational intelligence provides the biomedical domain by a significant support. The application of machine learning techniques in medical applications have been evolved from the physician needs. Screening, medical images, pattern classification, prognosis are some examples of health care support systems. Typically medical data has its own characteristics such as huge size and features, continuous and real attributes that refer to patients' investigations. Therefore, discretization and feature selection process are considered a key issue in improving the extracted knowledge from patients' investigations records. In this paper, a hybrid system that integrates Rough Set (RS) and Genetic Algorithm (GA) is presented for the efficient classification of medical data sets of different sizes and dimensionalities. Genetic Algorithm is applied with the aim of reducing the dimension of medical datasets and RS decision rules were used for efficient classification. Furthermore, the proposed system applies the Entropy Gain Information (EI) for discretization process. Four biomedical data sets are tested by the proposed system (EI-GA-RS), and the highest score was obtained through three different datasets. Other different hybrid techniques shared the proposed technique the highest accuracy but the proposed system preserves its place as one of the highest results systems four three different sets. EI as discretization technique also is a common part for the best results in the mentioned datasets while RS as an evaluator realized the best results in three different data sets.

Swarm Intelligence and Its Applications

Swarm Intelligence and Its Applications

A New Nonlinear Fuzzy Robust PCA Algorithm and Similarity Classifier in Classification of Medical Data Sets

In this article a classification method is proposed where data is first preprocessed using new nonlinear fuzzy robust principal component analysis (NFRPCA) algorithm to get data into more feasible form. After this preprocessing step the similarity classifier is then used for the actual classification. The procedure was tested for dermatology, hepatitis and liver-disorder data. Results were quite promising and better classification accuracy was achieved than using classical PCA and similarity classifier. This new nonlinear fuzzy robust principal component analysis algorithm seems to have the effect that it project the data sets into a more feasible form and when used together with the similarity classifier a classification accuracy of 72.27 % was achieved with liver-disorder data, 88.94 % with hepatitis, and 97.09 % accuracy was achieved with dermatology data. Compared to results with classical PCA and the similarity classifier, higher accuracies were achieved with the approach using nonlinear fuzzy robust principal component analysis and the similarity classifier.

A new feature selection method on classification of medical datasets: Kernel F-score feature selection

In this paper, we have proposed a new feature selection method called kernel F-score feature selection (KFFS) used as pre-processing step in the classification of medical datasets. KFFS consists of two phases. In the first phase, input spaces (features) of medical datasets have been transformed to kernel space by means of Linear (Lin) or Radial Basis Function (RBF) kernel functions. By this way, the dimensions of medical datasets have increased to high dimension feature space. In the second phase, the F-score values of medical datasets with high dimensional feature space have been calculated using F-score formula. And then the mean value of calculated F-scores has been computed. If the F-score value of any feature in medical datasets is bigger than this mean value, that feature will be selected. Otherwise, that feature is removed from feature space. Thanks to KFFS method, the irrelevant or redundant features are removed from high dimensional input feature space. The cause of using kernel functions transforms from non-linearly separable medical dataset to a linearly separable feature space. In this study, we have used the heart disease dataset, SPECT (Single Photon Emission Computed Tomography) images dataset, and Escherichia coli Promoter Gene Sequence dataset taken from UCI (University California, Irvine) machine learning database to test the performance of KFFS method. As classification algorithms, Least Square Support Vector Machine (LS-SVM) and Levenberg–Marquardt Artificial Neural Network have been used. As shown in the obtained results, the proposed feature selection method called KFFS is produced very promising results compared to F-score feature selection.

A New Approach to Diagnosing of Importance Degree of Obstructive Sleep Apnea Syndrome: Pairwise AIRS and Fuzzy-AIRS Classifiers

Artificial Immune Recognition System (AIRS) classifier algorithm is robust and effective in medical dataset classification applications such as breast cancer, heart disease, diabetes diagnosis etc. In our previous work, we have proposed a new resource allocation mechanism called fuzzy resource allocation in AIRS algorithm both to improve the classification accuracy and to decrease the computation time in classification process. Here, AIRS and Fuzzy-AIRS classifier algorithms and one against all approach have been combined to increase the classification accuracy of obstructive sleep apnea syndrome (OSAS) that is an important disease that influences both the right and the left cardiac ventricle. The OSAS dataset consists of four classes including of normal (25 subjects), mild OSAS (AHI (Apnea and Hypoapnea Index) = 5-15 and 14 subjects), moderate OSAS (AHI < 15-30 and 18 subjects), and serious OSAS (AHI > 30 and 26 subjects). In the extracting of features that is characterized the OSAS disease, the clinical features obtained from Polysomnography used diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering from this disease have been used. The used clinical features are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. Even though AIRS and Fuzzy-AIRS classifiers have been used in the classifying multi-class problems, theirs classification performances are low in the case of multi-class classification problems. Therefore, we have used two classes in AIRS and Fuzzy-AIRS classifiers by means of one against all approach instead of four classes comprising the healthy subjects, mild OSAS, moderate OSAS, and serious OSAS. We have applied the AIRS, Fuzzy-AIRS, AIRS with one against all approach (Pairwise AIRS), and Fuzzy-AIRS with one against all approach (Pairwise Fuzzy-AIRS) to OSAS dataset. The obtained classification accuracies are 63.41%, 63.41%, 87.19%, and 84.14% using the above methods for 200 resources, respectively. These results show that the best method for diagnosis of OSAS is the combination of AIRS and one against all approach (Pairwise AIRS).

Similarity classifier using similarity measure derived from Yu's norms in classification of medical data sets

A new approach using a similarity measure based on Yu's norms is presented for the detection of erythemato-squamous diseases, diabetes, breast cancer, lung cancer and lymphography. The domain contains records of patients with known diagnoses. The results are very promising with all data sets and (in conclusion, can be drawn that) a similarity model derived from Yu's norms could be used for the diagnosis of patients taking into consideration the error rate. A similarity classifier derived from Yu's norms was used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. The results confirmed that the proposed model has potential in detecting the erythemato-squamous diseases. The similarity model derived from Yu's norms achieved an accuracy rate ( 97.8 % ) which was higher than that of the stand-alone neural network model or the ANFIS model suggested in Übeyli and Güler [Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems, Comput. Biol. Med. 35 (2005) 421–433] or the similarity model based on Łukasiewicz similarity [Luukka and Leppälampi, Similarity classifier with generalized mean applied to medical data, Comput. Biol. Med. 36 (2006) 1026–1040]. With PIMA Indian diabetes, the detection model has an error rate of about 24 % which is much better than the overall rate of 33 % for diabetes. Also, a classifier was applied to the lung cancer data set and the results were to my knowledge better than before. When the lung cancer data were preprocessed with an entropy minimization technique and the classifier with similarity based on Yu's norm was applied, 99.99 % accuracy was achieved. The use of this preprocessing method enhanced the results over 30 % . In lymphography, entropy minimization also enhanced the results remarkably and 86.2 % accuracy was achieved.