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A Random Forest based predictor for medical data classification using feature ranking

Informatics in Medicine Unlocked 2019年 15卷

作者： Alam, Md. Zahangir Rahman, M. Saifur Rahman, M. Sohel Department of CSE BUET ECE Building West Palasi Dhaka 1205 Bangladesh

medical data classification is considered to be a challenging task in the field of medical informatics. Although many works have been reported in the literature, there is still scope for improvement. In this paper, a feature ranking based approach is developed and implemented for medical data classification. The features of a dataset are ranked using some suitable ranker algorithms, and subsequently the Random Forest classifier is applied only on highly ranked features to construct the predictor. We have conducted extensive experiments on 10 benchmark datasets and the results are promising. We present highly accurate predictors for 10 different diseases, as well as suggest a methodology that is sufficiently general and is expected to perform well for other diseases with similar datasets. © 2019

关键词： Disease predictors Feature ranking medical data classification Random forest

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Handling Imbalance and Limited data in Thyroid Ultrasound and Diabetic Retinopathy datasets Using Discrete Levy Flights Grey Wolf Optimizer Based Random Forest for Robust medical data classification

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Shobha Aswal Neelu Jyothi Ahuja Ritika Mehra Research Scholar Computer Science and Engineering VMSB Uttarakhand Technical University Dehradun India School of Computer Science University of Petroleum and Energy Studies Dehradun India School of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun India

In the field of disease diagnosis, medical image classification faces an inherent challenge due to various factors involving data imbalance, image quality variability, annotation variability, and limited data availability and data representativeness. Such challenges affect the algorithm's classification ability on the medical images in an adverse way, which leads to biased model outcomes and inaccurate interpretations. In this paper, a novel Discrete Levy Flight Grey Wolf Optimizer (DLFGWO) is combined with the Random Forest (RF) classifier to address the above limitations on the biomedical datasets and to achieve better classification rate. The DLFGWO-RF resolves the image quality variability in ultrasound images and limits the inaccuracies on classification using RF by handling the incomplete and noisy data. The sheer focus on the majority class may lead to unequal distribution of classes and thus leads to data imbalance. The DLFGWO balances such distribution by leveraging grey wolves and its exploration and exploitation capabilities are improved using Discrete Levy Flight (DLF). It further optimizes the classifier's performance to achieve balanced classification rate. DLFGWO-RF is designed to perform classification even on limited datasets, thereby the requirement of numerous expert annotations can thus be reduced. In diabetic retinopathy grading, the DLFGWO-RF reduces disagreements in annotation variability using subjective interpretations. However, the representativeness of the diabetic retinopathy dataset fails to capture the entire population diversity, which limits the generalization ability of the proposed DLFGWO-RF. Thus, fine-tuning of RF can robustly adapt to the subgroups in the dataset, enhancing its overall performance. The experiments are conducted on two widely used medical image datasets to test the efficacy of the model. The experimental results show that the DLFGWO-RF classifier achieves improved classification accuracy between 90-95%, which outp

关键词： medical data classification Grey Wolf Optimizer Discrete Levy Flights data Imbalance Image Quality Variability Limited data Availability

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Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for classification of Small-Sample medical Imaging data

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第2期34卷 586-600页

作者： Thammasorn, Phawis Chaovalitwongse, Wanpracha A. Hippe, Daniel S. Wootton, Landon S. Ford, Eric C. Spraker, Matthew B. Combs, Stephanie E. Peeken, Jan C. Nyflot, Matthew J. Univ Arkansas Dept Ind Engn Fayetteville AR 72701 USA Univ Washington Dept Radiol Seattle WA 98195 USA Univ Washington Dept Radiat Oncol Seattle WA 98195 USA Washington Univ Dept Radiat Oncol St Louis MO 63130 USA Tech Univ Munich TUM Dept Radiat Oncol D-81675 Munich Germany Helmholtz Zentrum Munchen Dept Radiat Sci DRS Inst Radiat Med IRM D-85764 Neuherberg Germany

Multi-view classification with limited sample size and data augmentation is a very common machine learning (ML) problem in medicine. With limited data, a triplet network approach for two-stage representation learning has been proposed. However, effective training and verifying the features from the representation network for their suitability in subsequent classifiers are still unsolved problems. Although typical distance-based metrics for the training capture the overall class separability of the features, the performance according to these metrics does not always lead to an optimal classification. Consequently, an exhaustive tuning with all feature-classifier combinations is required to search for the best end result. To overcome this challenge, we developed a novel nearest-neighbor (NN) validation strategy based on the triplet metric. This strategy is supported by a theoretical foundation to provide the best selection of the features with a lower bound of the highest end performance. The proposed strategy is a transparent approach to identify whether to improve the features or the classifier. This avoids the need for repeated tuning. Our evaluations on real-world medical imaging tasks (i.e., radiation therapy delivery error prediction and sarcoma survival prediction) show that our strategy is superior to other common deep representation learning baselines [i.e., autoencoder (AE) and softmax]. The strategy addresses the issue of feature's interpretability which enables more holistic feature creation such that the medical experts can focus on specifying relevant data as opposed to tedious feature engineering.

关键词： Feature extraction Training Task analysis Measurement Biomedical imaging Estimation Artificial neural networks medical data classification multi-view learning representation learning transfer learning metric learning

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Fuzzy Logic with Archimedes Optimization Based Biomedical data classification Model

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Computers, Materials & Continua 2022年第8期72卷 4185-4200页

作者： Mahmoud Ragab Diaa Hamed Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Department of Mineral Resources and Rocks Faculty of Earth SciencesKing Abdulaziz UniversityJeddah 21589Saudi Arabia Geology Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt

medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision ***,the advances of machine learning(ML)techniques assist to perform the effective classification *** this motivation,this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm(FCA-BFS)with optimal support vector machine(OSVM)model,named FCABFS-OSVM for medical data *** proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification ***,the proposed FCABFSOSVM technique involves the design of FCABFS technique to cluster the medical data which helps to boost the classification ***,the OSVM model investigates the clustered medical data to perform classification ***,Archimedes optimization algorithm(AOA)is utilized to the SVM parameters and boost the medical data classification results.A wide range of simulations takes place to highlight the promising performance of the FCABFS-OSVM *** comparison studies reported the enhanced outcomes of the FCABFS-OSVM technique over the recent state of art approaches.

关键词： Clustering medical data classification machine learning parameter tuning support vector machines

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Multi-layer perceptron classification method of medical data based on biogeography-based optimization algorithm with probability distributions

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APPLIED SOFT COMPUTING 2022年 121卷 108766-108766页

作者： Li, Xu-Dong Wang, Jie-Sheng Hao, Wen-Kuo Wang, Min Zhang, Min Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

In the field of medical informatics, the accuracy of medical data classification plays a vital role. Multi-layer Perceptron (MLP), as one of the most widely used neural networks, has been widely used in the medical fields. In recent years, the Biogeography-based Optimization (BBO) algorithm has been proposed to train MLP, but the original algorithm often encounters local minimums, slow convergence, and sensitivity to initialized values during the optimization process. To this end, this paper adopted the different probability distributions to improve the BBO (PD-BBO) algorithm to train MLP so as to improve medical data classification accuracy. These distributions include Gamma distribution, Beta distribution, Gaussian distribution, Exponential distribution, Poisson distribution, Geometric distribution, Rayleigh distribution and Weber distribution Then these different probability distributions were embed into the migration process of the BBO algorithm to replace the random distribution and the migration probability was defined. Finally, simulation experiments were carried out, and the benchmark function was used to prove the effectiveness of the proposed algorithms. And then it was used to train a multi-layer perceptron, and five medical data sets were selected for classification. After that, the performance of the standard BBO algorithm and five typical meta-heuristic algorithms were compared. The results showed that the PD-BBO algorithms to train MLP was better than the BBO algorithm and the selected meta-heuristic algorithms, and the classification accuracy has been improved to a certain extent. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Biogeography-based optimization algorithm Multi-layer perceptron Probability distribution medical data classification

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Feature Subset Selection with Artificial Intelligence-Based classification Model for Biomedical data

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Computers, Materials & Continua 2022年第9期72卷 4267-4281页

作者： Jaber S.Alzahrani Reem M.Alshehri Mohammad Alamgeer Anwer Mustafa Hilal Abdelwahed Motwakel Ishfaq Yaseen Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMahayil62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making *** latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification *** the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality *** resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)*** FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier ***,the FSS-AICBD technique undergoes min-max normalization technique to prevent data *** addition,the information gain(IG)approach is applied for the optimal selection of feature ***,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO *** choice of IG and GSO approaches results in promising medical data classification *** experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare *** simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

关键词： medical data classification feature selection deep learning healthcare sector artificial intelligence

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Assessment of prediction ability for reduced probabilistic neural network in data classification problems

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SOFT COMPUTING 2017年第1期21卷 199-212页

作者： Kusy, Maciej Kluska, Jacek Rzeszow Univ Technol Fac Elect & Comp Engn Al Powstancow Warszawy 12 PL-35959 Rzeszow Poland

One of the most important problems in probabilistic neural network (PNN) operation is the minimization of its structure. In this paper, two heuristic approaches of PNN's pattern layer reduction are applied. The first method is based on a k-means clustering procedure. In the second approach, the candidates for the network's pattern neurons are selected on the basis of a support vector machines algorithm. Modified models are compared in the classification problems with the traditional PNN, four well-known computational intelligence algorithms (single decision tree, multilayer perceptron, support vector machines, k-means algorithm) and PNN trained by the state-of-the-art procedures. Seven medical benchmark databases are investigated and one authors' own real ovarian cancer data set. Comparison is performed on the basis of the global performance indices which depend on the accuracy, sensitivity and specificity. These indices are computed using the standard tenfold cross-validation procedure. On the basis of the reported results, we show that the algorithm based on k-means clustering is a better PNN structure reduction procedure. Furthermore, this algorithm is much less time-consuming.

关键词： Probabilistic neural network k-Means clustering Support vector machines medical data classification Global performance index Accuracy Sensitivity Specificity

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Effective and efficient classification of gastrointestinal lesions: combining data preprocessing, feature weighting, and improved ant lion optimization

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第9期12卷 8683-8698页

作者： Singh, Dalwinder Singh, Birmohan St Longowal Inst Engn & Technol Longowal Punjab India

This paper presents an approach that combines the data preprocessing, feature weighting, and the improved antlion optimization algorithm for effective and efficient classification of gastrointestinal lesions. A high-dimensional gastrointestinal lesion dataset that consists of extracted texture, color, and shape features from the colonoscopy videos is obtained from the UCI repository. The data has certain imperfections such as the presence of zero-valued features, outliers, and dominant features. So, it is preprocessed to cope with these problems. Then, feature weighting is used to boost the classification performance by assigning the weights to the features according to their relevance in classification. The improved antlion optimization algorithm is used to search for feature weights and the parameters of the Support Vector Machines simultaneously. The experiments are performed using different combinations of features and endoscopic images to analyse the performance. The outcomes show that the combination of texture and color features from NBI images is the best. The accuracy of 97.37% and 98.68% for multi-class and binary classification problems respectively is attained using only similar to 31% features. Moreover, feature reduction helps to lower the runtime of the classifier by approx. 60%. In conclusion, a better approach is presented for colorectal lesions classification that competes with well-experienced colonoscopists and outperforms the existing methods.

关键词： Colorectal cancer data preprocessing High dimensional data medical data classification Support vector machines

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Oppositional firefly optimization based optimal feature selection in chronic kidney disease classification using deep neural network

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第4期13卷 1799-1810页

作者： Lambert, Jerlin Rubini Perumal, Eswaran Alagappa Univ Dept Comp Applicat Karaikkudi Tamil Nadu India

Nowadays, medical data classification plays an important role in healthcare informatics applications such as disease prediction, classification, etc. The recently developed machine learning and deep learning models are commonly employed for effective medical data classification, which can be applied for disease diagnosis. The existing techniques make few shortcomings in terms of computational complexity, higher-dimensional features, higher execution time, etc. To tackle these issues, this paper develops a new classification model with metaheuristic algorithm based optimal feature selection for Chronic Kidney Disease diagnosis. Initially, the data with missing values were evacuated in the pre-processing stage. Then, the best subset of features selected by a metaheuristic algorithm named Oppositional based FireFly Optimization algorithm, which helps in the prediction or classification of the disease more accurately. The incorporation of oppositional based learning concept helps to improve the convergence rate of FireFly algorithm. For classification, Deep Neural Network was proposed to diagnose the existence of CKD. The effectiveness of the proposed feature selection-based classifier was tested on the dataset as measures of sensitivity, specificity, and accuracy. The results concluded that the proposed algorithm achieved a high accuracy rate when compared to the algorithms of existing classifier models.

关键词： medical data classification CKD dataset OFFO algorithm DNN classifier

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BLSF: Adaptive Learning for Small-Sample medical data With Broad Learning System Forest Integration

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IEEE ACCESS 2024年 12卷 180844-180863页

作者： Saputra, Dimas Chaerul Ekty Sunat, Khamron Ratnaningsih, Tri Khon Kaen Univ Coll Comp Dept Comp Sci Khon Kaen 40002 Thailand Gadjah Mada Univ Fac Med Dept Clin Pathol & Lab Med Yogyakarta 55281 Indonesia

The Broad Learning System Forest (BLSF) model proved to be the preeminent classifier across all assessed datasets, demonstrating outstanding performance and efficiency. In the dataset, BLSF attained an accuracy of 94.53%, markedly exceeding standard BLS at 84.32%, Fuzzy BLS at 79.88%, and Intuitionistic Fuzzy BLS, which obtained just 15.55%. BLSF's efficacy is exemplified by its recall of 0.9452, accuracy of 0.9524, and F1-Score of 0.9481, emphasizing its ability to accurately categorize both positive and negative samples. Despite Tree BLS demonstrating a superior accuracy of 97.19%, its substantial memory use (789,862 MB) constrains its usability. In the Breast cancer dataset, BLSF attained a notable accuracy of 98.24%, trailing only Tree BLS at 98.75%, and markedly surpassing BLS (94.95%) and Fuzzy BLS (78.68%). The AUC-ROC score of BLSF at 0.9817 further substantiates its exceptional categorization capability. Although Incremental Weighted Ensemble BLS achieved a commendable performance of 98.57%, BLSF exhibited enhanced memory efficiency and a somewhat improved F1-Score of 0.9827. The Cardiovascular disease dataset revealed that BLSF achieved an accuracy of 76.61%, surpassing BLS at 71.38% and Fuzzy BLS at 53.23%. Although Tree BLS attained a superior accuracy of 91.75%, its considerable memory demands made BLSF the more efficient option. Incremental Weighted Ensemble BLS exhibited enhanced accuracy (81.99%) but required greater memory utilization. In imbalanced datasets such as PIDD (73.60%), Kidney failure (100%), and Heart disease (91.12%), BLSF consistently surpassed Fuzzy BLS and Intuitionistic Fuzzy BLS. It got a flawless score on the Kidney failure dataset, demonstrating its proficiency in managing difficult classifications with exceptional accuracy. BLSF excelled in accuracy, precision, recall, and efficiency across many datasets. In conclusion, the Broad Learning System Forest (BLSF) is a pioneering model that excels in accuracy and efficiency while exh

关键词： Radio frequency Accuracy medical diagnostic imaging medical services Predictive models Machine learning Learning systems Training Forestry Feature extraction Broad learning system forest feature mapping adaptive learning medical data classification small-sample classification enhanced predictive modeling

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