Biomedical Data Classification Research Articles

Classification of high-dimensional imbalanced biomedical data based on spectral clustering SMOTE and marine predators algorithm

The research of biomedical data is crucial for disease diagnosis, health management, and medicine development. However, biomedical data are usually characterized by high dimensionality and class imbalance, which increase computational cost and affect the classification performance of minority class, making accurate classification difficult. In this paper, we propose a biomedical data classification method based on feature selection and data resampling. First, use the minimal-redundancy maximal-relevance (mRMR) method to select biomedical data features, reduce the feature dimension, reduce the computational cost, and improve the generalization ability; then, a new SMOTE oversampling method (Spectral-SMOTE) is proposed, which solves the noise sensitivity problem of SMOTE by an improved spectral clustering method; finally, the marine predators algorithm is improved using piecewise linear chaotic maps and random opposition-based learning strategy to improve the algorithm’s optimization seeking ability and convergence speed, and the key parameters of the spectral-SMOTE are optimized using the improved marine predators algorithm, which effectively improves the performance of the over-sampling approach. In this paper, five real biomedical datasets are selected to test and evaluate the proposed method using four classifiers, and three evaluation metrics are used to compare with seven data resampling methods. The experimental results show that the method effectively improves the classification performance of biomedical data. Statistical test results also show that the proposed PRMPA-Spectral-SMOTE method outperforms other data resampling methods.

An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These systems support healthcare professionals like radiologists in their decision-making process by efficiently detecting abnormalities as well as offering accurate and dependable information. These systems heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. These features can subsequently assist in the diagnosis of related medical conditions. The task of identifying patterns in biomedical data can be quite challenging due to the presence of numerous irrelevant or redundant features. Therefore, it is crucial to propose and then utilize a feature selection (FS) process in order to eliminate these features. The primary goal of FS approaches is to improve the accuracy of classification by eliminating features that are irrelevant or less informative. The FS phase plays a critical role in attaining optimal results in machine learning (ML)-driven CAD systems. The effectiveness of ML models can be significantly enhanced by incorporating efficient features during the training phase. This empirical study presents a methodology for the classification of biomedical data using the FS technique. The proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding Optimization (EHO), and a proposed hybrid algorithm of these two. These algorithms were previously employed; however, their effectiveness in addressing FS issues in predicting human diseases has not been investigated. The following evaluation focuses on the categorization of benign and malignant tumours using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. The five-fold cross-validation technique is employed to mitigate the risk of over-fitting. The evaluation of the proposed approach's proficiency is determined based on several metrics, including sensitivity, specificity, precision, accuracy, area under the receiver-operating characteristic curve (AUC), and F1-score. The best value of accuracy computed through the suggested approach is 97.96%. The proposed clinical decision support system demonstrates a highly favourable classification performance outcome, making it a valuable tool for medical practitioners to utilize as a secondary opinion and reducing the overburden of expert medical practitioners.

Deep centroid: a general deep cascade classifier for biomedical omics data classification.

Classification of samples using biomedical omics data is a widely used method in biomedical research. However, these datasets often possess challenging characteristics, including high dimensionality, limited sample sizes, and inherent biases across diverse sources. These factors limit the performance of traditional machine learning models, particularly when applied to independent datasets. To address these challenges, we propose a novel classifier, Deep Centroid, which combines the stability of the nearest centroid classifier and the strong fitting ability of the deep cascade strategy. Deep Centroid is an ensemble learning method with a multi-layer cascade structure, consisting of feature scanning and cascade learning stages that can dynamically adjust the training scale. We apply Deep Centroid to three precision medicine applications-cancer early diagnosis, cancer prognosis, and drug sensitivity prediction-using cell-free DNA fragmentations, gene expression profiles, and DNA methylation data. Experimental results demonstrate that Deep Centroid outperforms six traditional machine learning models in all three applications, showcasing its potential in biological omics data classification. Furthermore, functional annotations reveal that the features scanned by the model exhibit biological significance, indicating its interpretability from a biological perspective. Our findings underscore the promising application of Deep Centroid in the classification of biomedical omics data, particularly in the field of precision medicine. Deep Centroid is available at both github (github.com/xiexiexiekuan/DeepCentroid) and Figshare (https://figshare.com/articles/software/Deep_Centroid_A_General_Deep_Cascade_Classifier_for_Biomedical_Omics_Data_Classification/24993516).

An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique.

Lung cancer has the highest death rate of any other cancer in the world. Detecting lung cancer early can increase a patient's survival rate. The corresponding work presents the method for improving the computer-aided detection (CAD) of nodules present in the lung area in computed tomography (CT) images. The main aim was to get an overview of the latest tools and technologies used: acquisition, storage, segmentation, classification, processing, and analysis of biomedical data. After the analysis, a model is proposed consisting of three main steps. In the first step, threshold values and component labeling of 3D components were used to segment the lung volume. In the second step, candidate nodules are identified and segmented with an optimal threshold value and rule-based trimming. It also selects 2D and 3D features from the candidate segmented node. In the final step, the selected features are used to train the SVM and classify the nodes and classify the non-nodes. To assess the performance of the proposed framework, experiments were performed on the LIDC data set. As a result, it was observed that the number of false positives in the nodule candidate was reduced to 4 FP per scan with a sensitivity of 95%.

Multi-omics integration method based on attention deep learning network for biomedical data classification

Background and objectiveIntegrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one of the most promising multi-omics data integration analysis methods. However, existing DL-based studies almost integrate the multi-omics data by concatenation in the input data space or the learned feature space, ignoring the correlations between patients and omics. MethodsWe propose a novel multi-omics integration method, called Multi-omics Attention Deep Learning Network (MOADLN), which is used for biomedical data classification. Firstly, for each type of omics data, we use three fully-connected layers and the self-attention mechanism to reduce dimensionality, and construct the correlations between patients, respectively. Then, we apply the feature vector learned from self-attention to generate the initial category labels. Secondly, for the initial label predicted of each omics data, we use an effective Multi-Omics Correlation Discovery Network (MOCDN) to learn the cross-omic correlations in the label space. Finally, we use the softmax classifier for label prediction. ResultsWe demonstrate that our method outperforms several state-of-the-art methods on two datasets with mRNA expression data, DNA methylation data, and miRNA expression data. In addition, we identified essential biomarkers of relevant diseases by MOADLN, and the generality of MOADLN is also demonstrated in the KIRP and KIRC datasets. ConclusionsMOADLN jointly explores correlations between patients in intra-omics and correlations of cross-omics in label space, which is an effective DL-based classification of biomedical data.

A New Preprocessing Method for Diabetes and Biomedical Data Classification

People of all ages and socioeconomic levels, all over the world, are being diagnosed with type 2 diabetes at rates that are higher than they have ever been. It is possible for it to be the root cause of a wide variety of diseases, the most notable of which include blindness, renal illness, kidney disease, and heart disease. Therefore, it is of the utmost importance that a system is devised that, based on medical information, is capable of reliably detecting patients who have diabetes. We present a method for the identification of diabetes that involves the training of the features of a deep neural network between five and 10 times using the cross-validation training mode. The Pima Indian Diabetes (PID) data set was retrieved from the database that is part of the machine learning repository at UCI. In addition, the results of ten-fold cross-validation show an accuracy of 97.8%, a recall OF 97.8%, and a precision of 97.8% for PIMA dataset using RF algorithm. This research examined a variety of other biomedical datasets to demonstrate that machine learning may be used to develop an efficient system that can accurately predict diabetes. Several different types of machine learning classifiers, such as KNN, J48, RF, and DT, were utilized in the experimental findings of biological datasets. The findings that were obtained demonstrated that our trainable model is capable of correctly classifying biomedical data. This was demonstrated by achieving higher 99% accuracy, recall, and precision for parikson dataset.

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.

Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence's role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients' mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.

Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

Biomedical data classification has become a hot research topic in recent years, thanks to the latest technological advancements made in healthcare. Biomedical data is usually examined by physicians for decision making process in patient treatment. Since manual diagnosis is a tedious and time consuming task, numerous automated models, using Artificial Intelligence (AI) techniques, have been presented so far. With this motivation, the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data. Besides, the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection (CMBO-FS) technique to derive a useful subset of features. In addition, Ridge Regression (RR) model is also utilized as a classifier to identify the existence of disease. The novelty of the current work is its designing of CMBO-FS model for data classification. Moreover, CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy. The results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.

A binary Bi-phase mutation-based hybrid Equilibrium Optimizer for feature selection in medical datasets classification

With the rapid expansion in Biological Sciences, biomedical data classification has become challenging. These datasets generally consist of missing values, redundant features and irrelevant information. A novel hybrid wrapper-based feature selection method is proposed to tackle these issues effectively. In order to improve the exploration ability of the particles, the Sine factor is integrated with the Equilibrium Optimizer (EO) technique. A Bi-phase Mutation (BM) scheme is integrated to enhance the exploitation phase of the EO algorithm (BM-based Hybrid EO, BMHEO). The BMHEO method is evaluated by employing four different classifiers – KNN, SVM, Random Forest (RF) and Discriminant Analysis (DA). It is observed that the Random Forest classifier exhibits superior performance compared to the other three classifiers. Eight S-shaped and V-shaped transfer functions are integrated to convert the solutions to binary form. Based on the above enhancements, eight different versions of BMHEO are produced. The performance of these versions is assessed using twenty biomedical datasets. Experiments on these datasets demonstrate that the BMHEO-S2 version outperforms other methods in terms of fitness values and classification accuracies. This approach is also tested on a MED-NODE image dataset for classification, and higher accuracy of 97.02% is achieved.

High-dimensional imbalanced biomedical data classification based on P-AdaBoost-PAUC algorithm

High-dimensional imbalanced biomedical data classification based on P-AdaBoost-PAUC algorithm

Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data

Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking. Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions. Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label extended text classification. There are 15 various single models consisting of three deep learning (CNN, LSTM, and BERT) and four machine learning algorithms (SVM, kNN, Decision Tree, and Naïve Bayes) with various text representations (TF-IDF, Word2Vec, and FastText). We used the bagging approach with a hard voting mechanism for the decision-making. Results: The result shows that deep learning is more powerful than machine learning as a single multi-label biomedical data classification method. Moreover, we found that top-three was the best number of base learners by combining the ensembles method. Heterogeneous-based ensembles with three learners resulted in an F1-score of 82.3%, which is better than the best single model by CNN with an F1-score of 80%. Conclusion: A multi-label classification of biomedical QA using ensemble models is better than single models. The result shows that heterogeneous ensembles are more potent than homogeneous ensembles on biomedical QA data with long text dimensions. Keywords: Biomedical Question Classification, Ensemble Method, Heterogeneous Ensembles, Multi-Label Classification, Question Answering

Nature-inspired metaheuristics model for gene selection and classification of biomedical microarray data.

Identifying a small subset of informative genes from a gene expression dataset is an important process for sample classification in the fields of bioinformatics and machine learning. In this process, there are two objectives: first, to minimize the number of selected genes, and second, to maximize the classification accuracy of the used classifier. In this paper, a hybrid machine learning framework based on a nature-inspired cuckoo search (CS) algorithm has been proposed to resolve this problem. The proposed framework is obtained by incorporating the cuckoo search (CS) algorithm with an artificial bee colony (ABC) in the exploitation and exploration of the genetic algorithm (GA). These strategies are used to maintain an appropriate balance between the exploitation and exploration phases of the ABC and GA algorithms in the search process. In preprocessing, the independent component analysis (ICA) method extracts the important genes from the dataset. Then, the proposed gene selection algorithms along with the Naive Bayes (NB) classifier and leave-one-out cross-validation (LOOCV) have been applied to find a small set of informative genes that maximize the classification accuracy. To conduct a comprehensive performance study, proposed algorithms have been applied on six benchmark datasets of gene expression. The experimental comparison shows that the proposed framework (ICA and CS-based hybrid algorithm with NB classifier) performs a deeper search in the iterative process, which can avoid premature convergence and produce better results compared to the previously published feature selection algorithm for the NB classifier.

An efficient binary chimp optimization algorithm for feature selection in biomedical data classification

An efficient binary chimp optimization algorithm for feature selection in biomedical data classification

Random vector functional link with ε-insensitive Huber loss function for biomedical data classification

Background and objectiveBiomedical data classification has been a trending topic among researchers during the last decade. Biomedical datasets may contain several features noises. Hence, the conventional machine learning model cannot efficiently handle the presence of noise in datasets. Among the several machine learning model, the random vector functional link (RVFL) is one of the most popular and efficient models for task related to both classification and regression. Despite its excellent classification performance, its performance degrades while dealing with the datasets with noise. Researchers are searching for powerful models to minimize the influence of noise in datasets. Therefore, to enhance the classification ability of RVFL on noisy datasets, this paper suggests a novel random vector functional link with ε-insensitive Huber loss function (ε-HRVFL) for biomedical data classification problems. MethodsThe optimization problem of ε-HRVFL is reformulated as strongly convex minimization problems with a simple function iterative approach to find solutions. To have a better understanding of the scope of the biomedical data classification problem and potential solutions, we conducted experiments with three different types of label noise in biomedical datasets as well as a few non-biomedical datasets. The classification accuracy of the proposed ε-HRVFL model is compared statistically using Friedman test with the support vector machine, extreme learning machine with radial basis function (RBF) and sigmoid activation functions and RVFL with RBF and sigmoid activation functions. ResultsFor non-biomedical datasets, the proposed model showed the highest accuracy of 98.1332%. Moreover, for the biomedical datasets, the proposed model showed the best accuracy of 96.5229%. The proposed ε-HRVFL model with sigmoid activation function reveals the best mean ranks among the reported classifiers for both, biomedical and non-biomedical datasets. ConclusionNumerical results show the applicability of the proposed ε-HRVFL model. In future, the proposed ε-HRVFL can be developed to solve multiclass biomedical data classification problems. Moreover, ε-insensitive asymmetric Huber loss function based RVFL model can be developed for dealing more efficiently with these noisy biomedical datasets.

Manta Ray Foraging Optimization with Machine Learning Based Biomedical Data Classification

The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources. The developments of artificial intelligence (AI) and machine learning (ML) models assist in the effectual design of medical data classification models. Therefore, this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-to-Sequence Autoencoder (OSAE-LSTM) model for biomedical data classification. The presented OSAE-LSTM model intends to classify the biomedical data for the existence of diseases. Primarily, the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform format. Followed by, the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical data. At last, manta ray foraging optimization (MRFO) algorithm has been employed for hyperparameter optimization process. The utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM model. The simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAE-LSTM model over the other approaches under several dimensions.

Fuzzy Logic with Archimedes Optimization Based Biomedical Data Classification Model

Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making. Besides, the advances of machine learning (ML) techniques assist to perform the effective classification task. With 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 classification. The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models. Besides, the proposed FCABFS-OSVM technique involves the design of FCABFS technique to cluster the medical data which helps to boost the classification performance. Moreover, the OSVM model investigates the clustered medical data to perform classification process. Furthermore, 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 technique. Extensive comparison studies reported the enhanced outcomes of the FCABFS-OSVM technique over the recent state of art approaches.

Convergence of Blockchain, k-medoids and homomorphic encryption for privacy preserving biomedical data classification

Data privacy on the Internet of Medical Things (IoMT) remains a critical concern when handling biomedical data. While extant studies focus on cryptography and differential privacy, few of them capture the utility and authenticity of data. As a result, data privacy remains the primary concern when training a machine learning (ML) model with IoMT data from various data sources/owners such as k − medoids. To overcome the above-mentioned issues, this study proposes secure ​k − medoids ​that are implemented together with Blockchain and partial homomorphic cryptosystem (Paillier) to ensure authenticity and protect all entities (i.e., data owner and data analyst) data privacy. The homomorphic property of Paillier is utilized to develop secure building blocks (i.e., secure polynomial operations, secure comparison, and secure biasing operations) to ensure data privacy and eliminate dependency on any third parties. We utilized three different biomedical datasets, and these are (I) Heart Disease Data (HDD), (II) Diabetes Data (DD), and (III) Breast Cancer Wisconsin Data (BCWD). Rigorous security analysis demonstrates that secure ​k − medoids ​protect against sensitive data breaches. It also showed superior performance in both BCWD (Accuracy 97.80%, Precision 96.83%, and Recall 99.80%) and HDD (Accuracy 82.50%, Precision 81.28%, and Recall 80.50%) datasets, respectively. However, similar performance was not reflected in the case of the DD dataset. Furthermore, the study explains why such performance results are observed. In addition, the proposed system has been proven to take less execution time compared to the extant studies.

Evolutionary Optimization with Outlier Detection Based Deep Learning Model for Biomedical Data Classification

Evolutionary Optimization with Outlier Detection Based Deep Learning Model for Biomedical Data Classification

Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results. Primarily, the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity. In addition, the information gain (IG) approach is applied for the optimal selection of feature subsets. Also, 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 algorithm. The choice of IG and GSO approaches results in promising medical data classification results. The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets. The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

Evolutionary optimisation with outlier detection-based deep learning model for biomedical data classification

Evolutionary optimisation with outlier detection-based deep learning model for biomedical data classification