Liver Disorders Dataset Research Articles

A non-linear optimization based robust attribute weighting model for the two-class classification problems.

This article aims to determine the coefficients that will reduce the in-class distance and increase the distance between the classes, collecting the data around the cluster centers with meta-heuristic optimization algorithms, thus increasing the classification performance. The proposed mathematical model is based on simple mathematical calculations, and this model is the fitness function of optimization algorithms. Compared to the methods in the literature, optimizing algorithms to obtain fast results is more accessible. Determining the weights by optimization provides more sensitive results than the dataset structure. In the study, the proposed model was used as the fitness function of the metaheuristic optimization algorithms to determine the weighting coefficients. In this context, four different structures were used to test the independence of the results obtained from the algorithm: the particle swarm algorithm (PSO), the bat algorithm (BAT), the gravitational search algorithm (GSA), and the flower pollination algorithm (FPA). As a result of these processes, a control group from unweighted attributes and four experimental groups from weighted attributes were obtained for each dataset. The classification performance of all datasets to which the weights obtained by the proposed method were applied increased. 100% accuracy rates were obtained in the Iris and Liver Disorders datasets used in the study. From synthetic datasets, from 66.9% (SVM classifier) to 96.4% (GSA Weighting + SVM) in the Full Chain dataset, from 64.6% (LDA classifier) to 80.2% in the Two Spiral datasets (weighted by BA + LDA). As a result of the study, it was seen that the proposed method successfully fulfills the task of moving the attributes to a linear plane in the datasets, especially in classifiers such as SVM and LDA, which have difficulties in non-linear problems, an accuracy rate of 100% was achieved.

CAD System for Liver Diseases using Histological and Imaging features

The current work for characterization of liver disease has been carried out using histological and imaging data. The BUPA liver disorders dataset created by University of California, Irvine has been considered as histological data. The ultrasound images of hepatocellular carcinoma (HCC) and Hemangioma (HEM) lesions were taken from ultrasoundcases.info. Laws ‘texture features were extracted from these images using Laws’ masks of length 3. In CAD system design 1, histological classification of liver diseases has been carried out using SVM classifier. In CAD system design 2, liver disease classification has been carried out using imaging features.

A new similarity-based classifier with Dombi aggregative operators

• A new classification method using Dombi aggregation operators is proposed and tested on real world datasets. • The most suitable Dombi operator for aggregation in each data set is determined. • The new classifier is efficient in classifying samples from both large and small datasets. • Classification accuracies obtained indicate an improvement compared to benchmark classifiers. In this paper we extend the similarity classifier to cover Dombi aggregation operators. The similarity classifier was earlier studied with the ordered weighted averaging (OWA) operator, the generalized mean, and other operators. We concentrate on the use of Dombi operators during aggregation of similarities within the classifier. Four Dombi aggregation operators applied here include: conjunctive, disjunctive, weighted conjunctive, and the product operator. From each of these operators, we form a variant of the similarity classifier. The proposed methods were tested on four real world medical datasets which include: fertility, lung cancer, Haberman’s survival, and liver disorder. The new classifiers achieved improved classification accuracies compared with earlier methods. Compared with the classifier using the generalized mean, the proposed method achieved an improvement of 17.80 % on fertility, 5.88 % on lung cancer, 0.65 % on Haberman’s survival, and 0.29 % on liver disorder datasets.

Neural network parameters optimization with genetic algorithm to improve liver disease estimation

Liver disease is an important public health problem. Over the past view decades machine learning has develop rapidly, and it has been introduced for application in medical-related fields. In this study we use neural network method to solve regression task of liver disorder dataset. Genetic algorithm applied for optimize NN parameters to improve the estimation performance value. NN-GA performance results show the most superior value compared to another methods.

A novel machine learning technique for computer-aided diagnosis

The primary motivation of this paper is twofold: first, to employ a heuristic optimization algorithm to optimize the dendritic neuron model (DNM) and second, to design a tidy visual classifier for computer-aided diagnosis that can be easily implemented on a hardware system. Considering that the backpropagation (BP) algorithm is sensitive to the initial conditions and can easily fall into local minima, we propose an evolutionary dendritic neuron model (EDNM), which is optimized by the gbest-guided artificial bee colony (GABC) algorithm. The experiments are performed on the Liver Disorders Data Set, the Wisconsin Breast Cancer Data Set, the Haberman’s Survival Data Set, the Diabetic Retinopathy Debrecen Data Set and Hepatitis Data Set, and the effectiveness of our model was rigorously validated in terms of the classification accuracy, the sensitivity, the specificity, the F_measure, Cohen’s Kappa, the area under the receiver operating characteristic curve (AUC), convergence speed and the statistical analysis of the Wilcoxon signed-rank test. Moreover, after training, the EDNM can simplify its neural structure by removing redundant synapses and superfluous dendrites by the neuronal pruning mechanism. Finally, the simplified structural morphology of the EDNM can be replaced by a logic circuit (LC) without sacrificing accuracy. It is worth emphasizing that once implemented by an LC, the model has a significant advantage over other classifiers in terms of speed when handling big data. Consequently, our proposed model can serve as an efficient medical classifier with excellent performance.

Improved XGBoost model based on genetic algorithm

An optimised XGBoost model based on genetic algorithm to search for optimal parameter combinations is proposed in this paper. It was proved that the improved algorithm has better classification effect than existing approaches through the liver disease data set Liver Disorders Data Set in the UCI Machine Learning Repository. In recent years, there have been many excellent intelligent algorithms in the field of machine learning and XGBoost is one of them. However, when using the XGBoost algorithm, it usually involves the adjustment of various parameters in the XGBoost model, and the classification performance of the model will be greatly influenced by the selection of parameters and their combination methods. In this paper, after encoding the XGBoost model parameters optimised by genetic algorithm, the global approximate optimal solution is obtained through operations such as selection, crossover and mutation, which greatly improves the performance of the model.

Improved XGBoost model based on genetic algorithm

An optimised XGBoost model based on genetic algorithm to search for optimal parameter combinations is proposed in this paper. It was proved that the improved algorithm has better classification effect than existing approaches through the liver disease data set Liver Disorders Data Set in the UCI Machine Learning Repository. In recent years, there have been many excellent intelligent algorithms in the field of machine learning and XGBoost is one of them. However, when using the XGBoost algorithm, it usually involves the adjustment of various parameters in the XGBoost model, and the classification performance of the model will be greatly influenced by the selection of parameters and their combination methods. In this paper, after encoding the XGBoost model parameters optimised by genetic algorithm, the global approximate optimal solution is obtained through operations such as selection, crossover and mutation, which greatly improves the performance of the model.

Bio-inspired weighed quantum particle swarm optimization and smooth support vector machine ensembles for identification of abnormalities in medical data

Knowledge mining is an emerging field where various patterns, rules, etc. can be generated which helps us in the analysis of the result. Medical information systems in modern hospitals and medical institutions become larger and larger, it causes great difficulties in extracting useful information for decision support. Especially, when traditional manual data analysis has become inefficient and methods for efficient computer-based analysis are indispensable. Decision support system is a suitable tool for medical application which helps in the extraction of useful data. A new hybrid model, weighted-quantum particle swarm optimization (WQPSO) for data clustering in sequence with smooth support vector machine (SSVM) is proposed for classification. The parameters considered to evaluate the clustering methods are inter-cluster distance, intra-cluster distance, and validation index. Experiments are performed in MATLAB and the performance analysis, comparisons are made with real-world datasets that are retrieved from UC Irvine Machine Learning Repository. The proposed approach overcomes the drawbacks of existing algorithms in terms of accuracy, performance and complexity. To validate the proposed algorithm, experiments are conducted on two data sets, i.e., liver disorders dataset and Wisconsin Breast Cancer Diagnosis (WBCD) dataset. The accuracy of proposed WQPSO-SSVM classification methodology is 83.76% for liver disorder, 98.42% for WBCD, 95.21% of mammography mass data. Among the considered algorithms such as WPSO-SVM, fuzzy, K-means, and fuzzy C-means methods, WQPSO-SSVM is found to yield a better convergence and provide an improved optimal solution.

An intelligent quality-based approach to fusing multi-source possibilistic information

Recently, Yager and Petry were proposing a quality-based methodology to combine data provided by multiple probabilistic sources to improve the quality of information for decision-makers. This paper offers a sort of companion paper that adapts this methodology to possibilistic sources. Possibility theory is particularly well suited to cope with incomplete information from poor-data sources. The methodology and algorithms used for the probabilistic approach are adapted for the possibilistic case. Both approaches are then compared by the means of a numerical example and four experimental benchmark datasets: one, the IRIS data set, being data-poorer than the three other ones (Diabetes dataset, Glass dataset and Liver-disorder dataset). A vector representation is introduced for a possibility distribution as in the probabilistic case and, the Gini's formulation of entropy is being used. However, the Gini's entropy has to be used differently than with the probabilistic case. This has an impact on the selection of subsets. A fusion scheme is designed to select the best-quality subsets according to two information quality factors: quantity of information and source credibility. Results obtained from comparison of both approaches on the four experimental benchmarks confirm the superiority of the possibilistic approach in the presence of information scarcity or incompleteness.

Comparative Performance Analysis of Various Classifiers for Cloud E-Health Users

Several classifiers are prevalent which act as a major drive for almost all supervised machine learning applications. These classifiers, though their objective working looks similar, they vary drastically in their performances. Some of the important factors that cause such variations are the scalability of the dataset, dataset nature, training time estimation, classifying time for the test data, prediction accuracy and the error rate computation. This paper focuses mainly on analyzing the performance of the existing four main classifiers: IF-THEN rule, C4.5 decision trees, naïve Bayes, and SVM classifier. The objective of this research article is to provide the complete statistical performance estimates of the four classifiers to the authenticated cloud users. These users have the access facility in obtaining the essential statistical information about the classifiers in study from the cloud server. Such statistical information might be helpful in choosing the best classifier for their personal or organizational benefits. The classifiers follow the traditional underlying algorithms for classification that is performed in the cloud server. These classifiers are tested on three different datasets namely PIMA, breast-cancer and liver-disorders dataset for performance analysis. The performance analysis indicators used in this research article to summarize the working of the various classifiers are training time, testing time, prediction accuracy and error rate computation. The proposed comparative analysis framework can be used to analyze the performances of the classifiers with respect to any input dataset.

Statistical Comparison of Classification Algorithms for Medical Datasets

The objective of this study is to compare the statistical performance of different classification methods on three medical datasets in terms of accuracy, recall, precision and F-score. The comparison is performed by building models using 32 different machine learning-based hybridised techniques and classification algorithms, which yields unbiased, accurate and reproducible results. For this purpose, well-known medical datasets; Wisconsin Breast Cancer dataset, Pima Indians Diabetes dataset and Liver Disorders dataset, are used. Different pre-processing approaches such as missing value imputation, normality test and feature selection are implemented in an open source package R for each dataset. The experimental results indicate an average of 10 runs of 10-fold cross-validation. Promisingly, this comprehensive study analyses the statistical performance of 32 classification techniques on each medical datasets.

Fuzzy Discretization based Classification of Medical Data

Discretization is one of the commonly used data preprocessing technique to improve the efficiency of the knowledge extraction process on clinical data. Generally, clinical data contains numeric attributes with continuous values. Data discretization simplifies the original data by transforming continuous data attribute values into a finite set of intervals. Although discretization is capable of handling continuous attributes on clinical data, there are cases where discretization is not an appropriate technique for handling continuous attributes. There are instances where attribute values are vague, imprecise and have multiple distributions with different classes, which challenges the process of mining in clinical data. Hence, there is a need for fuzzy discretization to pre-process the clinical data before mining. The aim of this study is to derive fuzzy discretization from crisp-interval discretization using geometric approach for constructing fuzzy sets, where overlapping region between the fuzzy sets is represented as geometric area. This study comprises of three steps: First, non-overlapping fuzzy sets are constructed using intervals generated from crisp-interval discretization. Second, area of overlapping between the fuzzy sets is computed based on the geometric approach and an average area of overlapping is estimated. Third, fuzzy sets are redesigned based on the estimated average area of overlapping. Fuzzy discretizations for three, five and seven intervals have been examined using Pima Indian Diabetes dataset (PID) and Bupa Liver Disorder dataset (BLD) taken from the University of California Irvine machine learning repository. The variation in performance of crisp and fuzzy discretization methods is measured using six classification approaches namely, tree based approach, probabilistic induction based approach, rule-based approach, network learning approach, kernel-based approach and distance-based approach and a rule-based fuzzy inference system. The results show that the classification accuracy remains stable with less deviation across different classifiers with varying intervals.

RBF Neural Network (RBFNN) using Density Based Clustering for Liver Disorder Dataset

Clustering is an unsupervised classification that is the partitioning of a data set in a set of meaningful subsets. Each object in dataset shares some common property- often proximity according to some defined distance measure. In this paper we will extend our previous work [15]. Simple K-means and Proposed makeDensityBased Clustering (MDBC) are embedded in RBF Neural Network (RBFNN). We evaluated the performance of RBFNN using K-Means and Proposed makeDensityBased Clustering on Liver Disorder Dataset. Proposed algorithm is superior to the existing makeDensityBased Clustering algorithm [15], but it is not capable of performing well when it is embedded with RBFNN.

DBSCAN BASED SEED INITIALIZATION OF K-MEANS ALGORITHM

This paper proposes effective approach to overcome the problem of finding initial number of clusters for Supervised Data mining algorithms. We present critical review of various approaches that finds the optimal number of clusters for clustering algorithms. In this paper we have used Dbscan algorithms to obtain initial seeds for basic k-means algorithm. To evaluate the proposed approache we have used iris data set, liver disorder dataset and seed dataset.

Generalized interaction LASSO based on alternating direction method of multipliers for liver disease classification

Features and interaction between features of liver disease is of great significance for the classification of liver disease. Based on least absolute shrinkage and selection operator (LASSO) and interaction LASSO, the generalized interaction LASSO model is proposed in this paper for liver disease classification and compared with other methods. Firstly, the generalized interaction logistic classification model was constructed and the LASSO penalty constraints were added to the interactive model parameters. Then the model parameters were solved by an efficient alternating directions method of multipliers (ADMM) algorithm. The solutions of model parameters were sparse. Finally, the test samples were fed to the model and the classification results were obtained by the largest statistical probability. The experimental results of liver disorder dataset and India liver dataset obtained by the proposed methods showed that the coefficients of interaction features of the model were not zero, indicating that interaction features were contributive to classification. The accuracy of the generalized interaction LASSO method is better than that of the interaction LASSO method, and it is also better than that of traditional pattern recognition methods. The generalized interaction LASSO method can also be popularized to other disease classification areas.

A neuron model with synaptic nonlinearities in a dendritic tree for liver disorders

With the number of deaths due to liver diseases increasing steadily in recent years, early detection and treatment of these diseases has been one of the most active research fields using computational intelligence techniques. In this paper, we propose a more realistic single‐neuron model with synaptic nonlinearities in a dendritic tree for liver disorder diagnosis. The computation on the neuron is performed as a combination of dimensional reduction and nonlinearity, which has a neuron‐pruning function that can remove useless synapses and dendrites during learning, forming a distinct synaptic and dendritic morphology. The nonlinear interactions in a dendrite tree are expressed using the Boolean logic AND (conjunction), OR (disjunction), and NOT (negation), which can be easily implemented in hardware. Furthermore, an error backpropagation (BP) algorithm is used to train the neuron model, and the performance is compared with a traditional BP neural network in terms of accuracy, sensitivity, and specificity. We use the BUPA liver disorder datasets obtained from the UCI Machine Learning Repository to verify the proposed method. Simulation results show promise for the use of this single‐neuron model as an effective pattern classification method in liver disorder diagnostics. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder Dataset

Accuracy in data classification depends on the dataset used for learning. Now-a-days the most important cause of death for both men and women is due to the Liver Problem. The healthcare industry collects a huge amount of data which is not properly mined and not put to the optimum use. Discovery of these hidden patterns and relationships often goes unexploited. Our research focuses on this aspect of Medical diagnosis by learning pattern through the collected data of Liver disorder to develop intelligent medical decision support systems to help the physicians. In this paper, we propose the use decision trees J48, Naive Bayes, ANN, ZeroR, 1BK and VFI algorithm to classify these diseases and compare the effectiveness, correction rate among them. Detection of Liver disease in its early stage is the key of its cure. It leads to better performance of the classification models in terms of their predictive or descriptive accuracy, diminishing of computing time needed to build models as they learn faster, and better understanding of the models. In this paper, a comparative analysis of data classification accuracy using Liver disorder data in different scenarios is presented. The predictive performances of popular classifiers are compared quantitatively.

A Swarm Optimization approach for clinical knowledge mining

Background and objectivesRule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm. MethodsWSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization. ResultsExperiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively. ConclusionWSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy.

Reverse Sequential Covering Algorithm for Medical Data Mining

Sequential Covering approach often follows general to specific approach to identify essential tuples and which are used to form a learning rule. Like other fields interpretation of sequential cover is more effective in health care also. Reverse technique will help to read instances from the last to generate appropriate rules. Alcoholic fatty liver disorder and Non-alcoholic fatty liver disorder dataset is used in this article for rule learning.

Reverse Sequential Covering Algorithm for Medical Data Mining

Sequential Covering approach often follows general to specific approach to identify essential tuples and which are used to form a learning rule. Like other fields interpretation of sequential cover is more effective in health care also. Reverse technique will help to read instances from the last to generate appropriate rules. Alcoholic fatty liver disorder and Non-alcoholic fatty liver disorder dataset is used in this article for rule learning.