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medical data classification using interval type-2 fuzzy logic system and wavelets

APPLIED SOFT COMPUTING 2015年 30卷 812-822页

作者： Thanh Nguyen Khosravi, Abbas Creighton, Douglas Nahavandi, Saeid Deakin Univ CISR Geelong Vic 3216 Australia

This paper introduces an automated medical data classification method using wavelet transformation (WT) and interval type-2 fuzzy logic system (IT2FLS). Wavelet coefficients, which serve as inputs to the IT2FLS, are a compact form of original data but they exhibits highly discriminative features. The integration between WT and IT2FLS aims to cope with both high-dimensional data challenge and uncertainty. IT2FLS utilizes a hybrid learning process comprising unsupervised structure learning by the fuzzy c-means (FCM) clustering and supervised parameter tuning by genetic algorithm. This learning process is computationally expensive, especially when employed with high-dimensional data. The application of WT therefore reduces computational burden and enhances performance of IT2FLS. Experiments are implemented with two frequently used medical datasets from the UCI Repository for machine learning: the Wisconsin breast cancer and Cleveland heart disease. A number of important metrics are computed to measure the performance of the classification. They consist of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. Results demonstrate a significant dominance of the wavelet-IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus useful as a decision support system for clinicians and practitioners in the medical practice. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Interval type-2 fuzzy logic system Wavelet transformation Genetic algorithm medical data classification Breast cancer Heart disease

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Impact of preprocessing on medical data classification

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Frontiers of Computer Science 2016年第6期10卷 1082-1102页

作者： Sarab ALMUHAIDEB Mohamed El Bachir MENAI Computer Science Department College of Computer and Information Sciences Prince Sultan University Riyadh 11586 Saudi Arabia

The significance of the preprocessing stage in any data mining task is well known. Before attempting medical data classification, characteristics of medical datasets, including noise, incompleteness, and the existence of multiple and possibly irrelevant features, need to be addressed. In this paper, we show that selecting the right combination of prepro- cessing methods has a considerable impact on the classification potential of a dataset. The preprocessing operations con- sidered include the discretization of numeric attributes, the selection of attribute subset（s）, and the handling of missing values. The classification is performed by an ant colony optimization algorithm as a case study. Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained.

关键词： classification ant colony optimization medical data classification preprocessing feature subset selection,discretization

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IWD-Miner: A Novel Metaheuristic Algorithm for medical data classification

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Computers, Materials & Continua 2021年第2期66卷 1329-1346页

作者： Sarab AlMuhaideb Reem BinGhannam Nourah Alhelal Shatha Alduheshi Fatimah Alkhamees Raghad Alsuhaibani Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia

medical data classification(MDC)refers to the application of classification methods on medical *** work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or *** gain experts’trust,the prediction and the reasoning behind it are equally ***,we confine our research to learn rule-based models because they are transparent and *** approach to MDC involves the use of metaheuristic(MH)*** we report on the development and testing of a novel MH algorithm:*** algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its *** this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California ***,there were only limited differences in performance between IWDMiner variants which is suggestive of its ***,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and *** experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical *** suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively).

关键词： Ant colony optimization AntMiner+ IWDs IWD-Miner J48 medical data classification metaheuristic algorithms swarm intelligence

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Integrating multiple methods to enhance medical data classification

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EVOLVING SYSTEMS 2020年第1期11卷 133-142页

作者： Tarle, Balasaheb Chintakindi, Sanjay Jena, Sudarson GITAM Deemed Be Univ CSE Dept Hyderabad Campus Hyderabad Telangana India GITAM Deemed Be Univ Sch Technol Hyderabad Campus Hyderabad Telangana India Sambalpur Univ SUIIT Comp Engn & Applicat Sambalpur Orissa India

In medical data classification, data reduction and improving classification performance are the important issues in the current scenario. In existing medical data classification methods, initially, the medical data pre-processing is performed. After pre-processing feature selection is performed, otherwise, the process is more time consuming and has poor accuracy. Here we have proposed two algorithms for enhancing the classification performance on medical data. In first proposed method Bag of Words technique is used for better feature subset selection. Subsequently, the hybrid Fuzzy-Neural Network approach used that can handle imprecision in data while classification. This combination of feature selection technique and Fuzzy-Neural Network classifier approach gives enhanced classification accuracy. In the second proposed algorithm, we have integrated data cleaning technique to improve data quality as pre-processing technique along with bag of words and Fuzzy-Neural Network, this method performs classification on clean filtered data with appropriately reduced feature set that results in more accurate classification than the existing methods. Thus in proposed approaches we have tried to handle three issues, removing noise in data, optimal feature subset selection and handling imprecision in data. The comparative study of various medical datasets in terms of accuracy shows that the two proposed algorithms perform better as compared to existing techniques and the enhancement obtained is around 3% and 17% respectively. In addition the performance of Bag of Words feature selection method used in the proposed system is compared with two feature selection methods LSFS and SFFS.

关键词： Classifier Fuzzy-neural network Bag of words medical data classification

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Artificial Rabbits Optimizer With Machine Learning Based Emergency Department Monitoring and medical data classification at KSA Hospitals

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IEEE ACCESS 2023年 11卷 59133-59141页

作者： Alharbi, Lubna A. A. Univ Tabuk Fac Comp & Informat Technol Dept Comp Sci Tabuk 71491 Saudi Arabia

The Emergency Departments (EDs) in health centres located in the main areas of Saudi Arabia have heavy patient inflow because of the pandemic, viral infections, and even on some special occasions like Umrah or Hajj, where pilgrims who travel from one place to another with serious disorders. Other than the EDs, it was important to observe the patient's activities from ED to other wards in the region or the hospital to track the spread of viral diseases. In this case, deep learning (DL) and machine learning (ML) methods have been used to track the target audience and classify the data into many classes. With this motivation, this study develops an artificial rabbit optimization with a machine learning-based healthcare data classification (AROML-HDC) technique for EDs. The AROML-HDC technique monitors and tracks the patient visit data, treatment given, and length of stay (LOS). In addition, the AROML-HDC technique designs an effective ARO algorithm for the optimal selection of feature subsets. Next, the class-specific cost regulation extreme learning machine (CSCR-ELM) classifier is applied for effective medical data classification. Finally, the grasshopper optimization algorithm (GOA) was used to adjust the parameters related to the CSCR-ELM classifier. The experimental outcome of the AROML-HDC approach is tested on the benchmark Cleveland dataset and the Statlog dataset comprising 297 and 270 samples, respectively. The simulation results signify the improved performance of the AROML-HDC technique over other recent methods with maximum accuracy of 93.22% and 94.05% Cleveland dataset and the Statlog dataset, respectively.

关键词： medical data classification emergency departments KSA hospitals feature selection machine learning

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Grammar-based automatic programming for medical data classification: an experimental study

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ARTIFICIAL INTELLIGENCE REVIEW 2021年第6期54卷 4097-4135页

作者： Si, Tapas Miranda, Pericles Galdino, Joao Victor Nascimento, Andre Bankura Unnayani Inst Engn Dept Comp Sci & Engn Bankura W Bengal India Univ Fed Rural Pernambuco UFRPE Dept Comp Recife PE Brazil

In a computational medical model, diagnosis is the classification of disease status in the terms of abnormal or positive, normal or negative or intermediate stages. Different Machine learning techniques such as artificial neural networks (ANNs) are extensively and successfully used in disease diagnosis. However, there is no single classifier that can solve all classification problems. Selecting an optimal classifier for a problem is difficult, and it has become a relevant subject in the area. This paper focuses on grammar-based automatic programming (GAP) to build optimized discriminant functions for medical data classification in any arbitrary language. These techniques have an implicit power of automatic feature selection and feature extraction. This work carries out an in-depth investigation of the use of different GAP algorithms in the medical data classification problem. The objective is to identify the benefits and limitations of algorithms of this nature in the current problem. Classical classifiers were also considered for comparison purposes. Fourteen medical data sets were used in the experiments, and seven performance measures such as accuracy, sensitivity, specificity, precision, geometric-mean, F-measure, and false-positive rate are used to evaluate the performance of the produced classifier. The multiple criteria decision analysis (MCDA) demonstrates that GAP approaches are able to produce suitable classifiers for a given problem, and the GS performs better than other classical classifiers in medical data classification.

关键词： Automatic programming Swarm programming Grammatical swarm medical data classification

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Energy Aware Clustering with medical data classification Model in IoT Environment

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Computer Systems Science & Engineering 2023年第1期44卷 797-811页

作者： R.Bharathi T.Abirami Department of Computer Science and Engineering Cheran College of EngineeringK.ParamathiKarur639111India Department of Information Technology Kongu Engineering CollegePerunduraiErode638060India

With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced *** to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly *** the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic *** this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT *** goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT *** proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct ***,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter *** design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the *** show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.

关键词： Internet of things healthcare medical data classification energy efficiency clustering autoencoder

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A novel fitness function in genetic programming for medical data classification

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JOURNAL OF BIOmedical INFORMATICS 2020年 112卷 103623-103623页

作者： Kumar, Arvind Sinha, Nishant Bhardwaj, Arpit Bennett Univ Dept Comp Sci Engn Greater Noida India Pitney Bowes Software Noida India

In the last decade, machine learning (ML) techniques have been widely applied to identify different diseases. This facilitates an early diagnosis and increases the chance of survival. The majority of medical data-sets are unbalanced. Due to this, ML classification techniques give biased classification over the majority class. In this paper, a novel fitness function in Genetic Programming, for medical data classification has been proposed that handles the problem of unbalanced data. Four benchmark medical data-sets named chronic kidney disease (CKD), fertility, BUPA liver disorder, and Wisconsin diagnostic breast cancer (WDBC) have been taken from the University of California (UCI) machine learning repository. classification is done using the proposed technique. The proposed technique achieved the best accuracy for CKD, WDBC, Fertility, and BUPA dataset as 100%, 99.12%, 85.0%, and 75.36% respectively, and the best AUC as 1.0, 0.99, 0.92, and 0.75 respectively. The result outcomes show an improvement over other GP and SVM methods that confirm the efficiency of our proposed algorithm.

关键词： medical data classification Genetic Programming Fitness function Unbalanced data classification

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Optimal weighted feature vector and deep belief network for medical data classification

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INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING 2020年第2期18卷

作者： Kadam, Vinod Jagannath Jadhav, Shivajirao Manikrao Dr Babasaheb Ambedkar Technol Univ Dept Informat Technol Lonere Maharashtra India

medical data classification is the process of transforming descriptions of medical diagnoses and procedures into universal medical code numbers. The diagnoses and procedures are usually taken from a variety of sources within the healthcare record, such as the transcription of the physician's notes, laboratory results, radiologic results and other sources. However, there exist many frequency distribution problems in these domains. Hence, this paper intends to develop an advanced and precise medical data classification approach for diabetes and breast cancer dataset. With the knowledge of the features and challenges persisting with the state-of-the-art classification methods, deep learning-based medical data classification methodology is proposed here. It is well known that deep learning networks learn directly from the data. In this paper, the medical data is dimensionally reduced using Principle Component Analysis (PCA). The dimensionally reduced data are transformed by multiplying by a weighting factor, which is optimized using Whale Optimization Algorithm (WOA), to obtain the maximum distance between the features. As a result, the data are transformed into a label-distinguishable plane under which the Deep Belief Network (DBN) is adopted to perform the deep learning process, and the data classification is performed. Further, the proposed WOA-based DBN (WOADBN) method is compared with the Neural Network (NN), DBN, Generic Algorithm-based NN (GANN), GADBN, Particle Swarm Optimization (PSONN), PSO-based DBN (PSODBN), WOA-based NN (WOANN) techniques and the results are obtained, which shows the superiority of proposed algorithm over conventional methods.

关键词： medical data classification principle component analysis distance measure Whale optimization algorithm deep belief network

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Developing Transformed Fuzzy Neural Networks to Enhance medical data classification Accuracy

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2018年第6期20卷 1925-1937页

作者： Huang, Yo-Ping Singh, Avichandra Liu, Shen-Ing Wu, Shu-I Hoang An Quoc Sereter, Andrea Natl Taipei Univ Technol Dept Elect Engn Taipei 10608 Taiwan Natl Taipei Univ Dept Comp Sci & Informat Engn New Taipei 23741 Taiwan Mackay Mem Hosp Dept Psychiat Taipei 10449 Taiwan Mackay Mem Hosp Dept Psychiat Taipei 10449 Taiwan Mackay Mem Hosp Ctr Suicide Prevent Taipei 10449 Taiwan Ho Chi Minh City Univ Technol & Educ Dept Thermal Engn Ho Chi Minh City Vietnam Obuda Univ Antal Bejczy Ctr Intelligent Robot Budapest Hungary

Developing an automatic model to precisely and quickly classify medical data is a challenging work due to lack of medical knowledge. This paper presented a transformed fuzzy neural network (TFNN) to enhance medical data classification accuracy by learning knowledge from the available medical databases. We developed a novel reward/penalty fuzzy rule base to infer the degree of transformation for each input attribute. The degree of reward/penalty was determined according to its original measured value relative to its attribute mean and standard deviation. The farther the deviation of a datum to its mean value, the larger the penalty was exerted on the datum. In contrast, a datum in the neighborhood of mean value received a positive award. Furthermore, a pattern's desired output was considered a valuable knowledge and was treated as an extra input to the TFNN. In this study, three medical datasets are used to validate the classification accuracy. Results compared to the back propagation neural network and support vector machine verified the effectiveness of the proposed model in medical data classification.

关键词： Fuzzy neural network (FNN) Fuzzy rule base Support vector machine (SVM) Neural network (NN) medical data classification

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