Medical Data Classification Research Articles

ProtAct-Us from serious injuries with long-term consequences

Abstract Battling serious injuries and long-term consequences in road traffic remains one important issue in the overall goal of resilient future road transport and its Vision Zero. Challenges of increasing personal transport in urban areas coupled with autonomous vehicles and shuttles, new mobility devices and the overall need to ensure the safety of vulnerable road users as well as all types of vehicle occupants’ require solid medical background knowledge paired with innovative engineering approaches. EU Horizon Europe is currently funding the ProtAct-Us consortium (2024-2027) that aims at protecting all Road User Groups from serious injury and long-term physical, cognitive and mental health consequences related to road crashes through innovatively interlinked research action between medical and engineering methods and approaches. ProtAct-Us works on the following goals and challenges: i) Medical data correlation, standardisation and classification of long-term consequences of road crashes; ii) Robust and reliable medical, epidemiological and engineering tools like agreed methods to collect information on long-term consequences, upgraded Human Body Models and virtual assessment methods for the extremities, head, neck and face allowing for effective countermeasure development for all road users; iii) Reduction of long-term consequences and related socio-economic cost of road crash related injuries for all road users. The ProtAct-Us solutions will influence new standards in respect of extending current injury codification system with relevant long-term aspects, allowing new physical as well as virtual safety assessment procedures and adaption of rules and regulations to be implemented. Finally, scientifically well-founded suggestion for future implementation into policy, regulatory, and standardization guidelines for an inclusive safety improvement approach for in- as well as post-crash will be provided.

EFFICIENCY AND ACCURACY OF CONVOLUTIONAL AND FOURIER TRANSFORM LAYERS IN NEURAL NETWORKS FOR MEDICAL IMAGE CLASSIFICATION

In an era where information flow is moving at a rapid pace, image data processing is becoming increasingly important as technology advances, including in healthcare. Convolutional Neural Network (CNN) has been a common approach in image classification, but the larger the volume of data and the complexity of the task, the more expensive the computational cost of CNN. With the rapid growth in the amount of image data, efficiency in data processing is becoming increasingly important. In this study, the performance of neural network models using the convolution layer and Fourier transform layer in medical image data classification was compared. The results show that models with a Fourier transform layer tend to provide higher accuracy and better Area Under Curve (AUC) compared to models using a convolution layer. In addition, the model with the Fourier transform layer also shows faster execution time per epoch, which indicates efficiency in data processing. However, the convolution layer has an advantage in terms of model size, although it is not significantly different from the Fourier transform layer. In conclusion, the Fourier transform layer has an advantage in the classification of medical image data.

Adam energy valley optimization-based routing and RF-Spinalnet enabled medical data classification in IoT

Adam energy valley optimization-based routing and RF-Spinalnet enabled medical data classification in IoT

A Big Data Intelligent Evaluation System for Sports Knowledge

The rapid evolution of computer technology has significantly impacted the field of medicine, particularly in the utilization of information and image evidence. In the realm of sports medicine, this technological advancement plays a crucial role in ensuring sports safety, especially in the context of injury recovery following sports-related activities. The necessity to interpret and utilize a vast amount of sports medical data effectively has emerged as a pivotal research avenue. This paper delves into the challenges associated with extracting, studying, and the accuracy training of complex algorithms essential for analyzing critical sporting medical data.Central to this discussion is introducing an Optimized Convolutional Neural Network (OCNN) model, which is based on deep learning principles. This model is designed to enhance the detection and risk assessment of diseases related to sport medicine. It incorporates a novel Self-Adjustment Resizing algorithm (SAR), augmented by a self-coding method of convolution (SCM). The proposed OCNN model comprises two convolutional layers, two pooling layers, a fully connected layer, and a SoftMax structure. This architecture is tailored for the classification and analysis of sport-related medical data.

Hybridization of Machine Learning Model with Bee Colony based Feature Selection for Medical Data Classification

Nowadays, an important count of biomedical data is created continuously in several biomedical equipment and experiments because of quick technical enhancements in biomedical science. The study of clinical and health data is vital to enhance the analysis precision, prevention, and treatment. Initial analysis and treatment are extremely important approaches for preventing deaths in many diseases. Accordingly, the data mining and machine learning (ML) approaches are helpful tools for utilizing minimization error and for providing helpful data for analysis. But the data obtained in digital machines takes higher dimensionality, and not every data attained in digital machines is significant to specific diseases. This article develops an artificial bee colony-based feature selection with optimal hybrid ML model for medical data classification (ABCFS-OHML) technique. The presented ABCFS-OHML technique mainly aims to identify and classify the presence of disease using medical data. To attain this, the presented ABCFS-OHML technique initially pre-processes the input data in two ways namely null value removal and data transformation. Furthermore, the presented ABCFS-OHML technique uses ABCFS model for the choice of effectual subset of features. At last, root means square propagation with convolutional neural network-Hop field neural network (CNN-HFNN) model for classification purposes. The usage of RMSProp optimizer assists in attaining optimal hyperparameter selection of the CNN-HFNN method. The performance validation of the ABCFS-OHML technique takes place using three medical datasets. The comparison study reported that the ABCFS-OHML technique has accurately classified the medical data over other recent approaches.

Medical Data NER and Classification Using Hybridized BERT Model

The extraction of important information from medical texts by Named Entity Recognition (NER) is a key component of advanced medical text processing. Medical practitioners rely heavily on NER's assistance with disease surveillance, clinical resolution building, and substantiation-based treatment. As the foundation of text information processing in the medical field, it guarantees precise location of data required for knowledgeable medical decisions and attentive disease surveillance. Additionally, a core goal in medical Natural Language Processing (NLP) is medical text categorization, which tries to classify short medical texts into distinct groups. Most recent work has concentrated on using pre-trained linguistic processes for text cataloging in medicine. The present work presents a novel clinical neural network architecture (NER) method that was created with a customized Rule Based BiLSTM-BERT (Bidirectional Encoder Representations from Transformers) architecture that incorporates Retrieval Augment Generation. Across several fields, including medicine, deep learning has demonstrated noteworthy advancements. These results show that, when applied to our test dataset, the BiLSTM-BERT-RAG model produced results that were almost human-like. The system proficiently recognized pertinent vocabulary representative of the intended protocol.

Advanced image analysis for medical diagnostics: a system for segmentation and classification using level set methods and AI algorithms

This work aims to implement and utilize an advanced computer system for image analysis and processing through artificial intelligence. The system will evaluate images from multiple sources. As a result, a comprehensive e-Medicus system will be developed to capture and analyze X-ray data and classify cancer cells. This innovative tool, with its unique features tailored for medical facilities, will assist them in capturing and analyzing X-ray images and CT scan results. The e-Medicus system offers several benefits for medical facilities, including efficient automation of photo analysis, tracking changes in a patient's condition over time, and facilitating the identification of medical changes and data classification. A multimedia presentation of the change process will use the contour set function, allowing for topological changes in solution properties. The system integrates novel procedures and algorithms from theoretical computer science and numerical mathematics, leveraging neural networks, genetic algorithms, semantic networks, image ontology, rough set theory, contour set methods, and hybrid algorithms. These developed algorithms enhance methods and concepts in image segmentation, gathering, transmitting, storing, extracting, and effectively presenting information.

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

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 outperforms the existing techniques for various imbalanced datasets.

Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification

The Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches.

Weighted Rank Difference Ensemble: A New Form of Ensemble Feature Selection Method for Medical Datasets

Background: Feature selection (FS), a crucial preprocessing step in machine learning, greatly reduces the dimension of data and improves model performance. This paper focuses on selecting features for medical data classification. Methods: In this work, a new form of ensemble FS method called weighted rank difference ensemble (WRD-Ensemble) has been put forth. It combines three FS methods to produce a stable and diverse subset of features. The three base FS approaches are Pearson’s correlation coefficient (PCC), reliefF, and gain ratio (GR). These three FS approaches produce three distinct lists of features, and then they order each feature by importance or weight. The final subset of features in this study is chosen using the average weight of each feature and the rank difference of a feature across three ranked lists. Using the average weight and rank difference of each feature, unstable and less significant features are eliminated from the feature space. The WRD-Ensemble method is applied to three medical datasets: chronic kidney disease (CKD), lung cancer, and heart disease. These data samples are classified using logistic regression (LR). Results: The experimental results show that compared to the base FS methods and other ensemble FS methods, the proposed WRD-Ensemble method leads to obtaining the highest accuracy value of 98.97% for CKD, 93.24% for lung cancer, and 83.84% for heart disease. Conclusion: The results indicate that the proposed WRD-Ensemble method can potentially improve the accuracy of disease diagnosis models, contributing to advances in clinical decision-making.

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

Objectives: The main objectives of this research endeavor encompass the development of the Adaptive RPL Optimization (ARPLO) model to enhance data transmission efficiency within IoT networks. This includes constructing a grid-based network structure optimized for data transfer, selecting the most suitable nodes as grid head nodes to maximize network lifespan while minimizing energy consumption, implementing an innovative objective function-driven approach to optimize parent node selection, and integrating an Adaptive Deep Neural Network (ADNN) to accurately classify medical data. Methods: The research methodology entails several key steps. A grid-based network structure is established with IoT nodes and root nodes, where a DODAG process incorporating DIO messages is utilized for node ranking. To enhance energy efficiency, the Trickle algorithm is employed for control message optimization. Grid head nodes are chosen based on metrics such as root node fairness, residual energy, and load influence index. The novel Middle Order Optimal Routing (MOOR) objective function is utilized to optimize routing decisions. ADNN is implemented for precise medical data classification. The proposed model's performance is evaluated through simulation in a Python environment. Findings: The research findings demonstrate that the ARPLO model yields notable benefits compared to existing models. It achieves higher energy efficiency, improved throughput, enhanced packet delivery ratio (PDR), and an extended network lifespan. The Trickle algorithm contributes to efficient control message optimization. The MOOR-based routing approach improves multimedia medical data transfer. Moreover, the integration of ADNN enhances the accuracy of data classification, particularly in healthcare applications. The research outcomes align with the broader field's existing values and reports while offering novel insights that contribute to enhancing the existing knowledge base. ARPLO protocol performance reveals that there is increase of throughput of 31.2%, PDR by 7.12%, lifetime of 10.7 % with reduction of energy consumption by 12.72%, control overhead by 31.01% and end-to-end delay by 33.01%. Novelty: The novelty of this research lies in its comprehensive approach that integrates a grid-based network structure, MOOR-based optimization, and ADNN-based classification. The incorporation of the Trickle algorithm for energy-efficient communication is an innovative feature. The introduction of new metrics for grid head node selection, along with the application of the MOOR objective function for multimedia medical data routing, showcases the research's innovative contributions. Keywords: Internet of Things (IoT), RPL (Routing Protocol for Low­Power and Lossy Networks), Optimization, Routing, Multimedia, Healthcare

Enhancing patient information performance in internet of things-based smart healthcare system: Hybrid artificial intelligence and optimization approaches

Utilizing cutting-edge technology in the healthcare industry could provide advanced methods for managing medical records for patients’ treatment. Integrating essential technologies like artificial intelligence (AI) can enhance healthcare services. The intersection of the Internet of Things (IoT) and AI presents various prospects within the healthcare industry. This research paper presents a novel model for enhancing the standard of treatment in smart healthcare systems (SHS) based on the convergence of AI and IoT. We investigate optimizing patient data processing performance in smart healthcare centers. The upgraded particle swarm optimization-long short-term memory (PSO-LSTM) algorithm is introduced to optimize the IoT-based SHS model. In order to perform a more suitable classification of the patient medical data, PSO is determined to be compared with PSO-LSTM to tune several metrics and benchmarks to achieve the highest value on patient data process performance. The proposed outcomes discovered that in terms of the test sets, the predicted values of patient health risks, and just six of them are varied, with an accuracy of 92.5%. This accuracy indicates the PSO-LSTM algorithm has more satisfactory performance and higher efficiency and provides a more secure, reliable, and improved overall patient satisfaction experience.

Multi-sensor Data Fusion based Medical Data Classification Model using Gorilla Troops Optimization with Deep Learning

Wireless Body Sensor Network (BSN) comprises wearables with different sensing, processing, storing, and broadcast abilities. Once several devices acquire the data, multi-sensor fusion was needed for transforming erroneous sensor information into maximum quality fused data. Deep learning (DL) approaches are utilized in different application domains comprising e-health for applications like activity detection, and disease forecast. In recent times, it can be demonstrated that the accuracy of classification techniques is enhanced by the combination of feature selection (FS) approaches. This article develops a Multi-sensor Data Fusion based Medical Data Classification Model using Gorilla Troops Optimization with Deep Learning (MDFMDC-GTODL) algorithm. The proposed MDFMDC-GTODL method enables collection of various daily activity data using different sensors, which are then fused to produce high-quality activity data. In addition, the MDFMDC-GTODL technique applies optimal attention based bidirectional long short term memory (ABLSTM) for heart disease prediction. In this study, Gorilla Troops Optimization Algorithm based FS (GTOA-FS) technique is involved to improve the classification performance. The simulation outcome of the MDFMDC-GTODL technique are validated and the results are investigated in different prospects. A wide-ranging simulation analysis stated the better performance of the MDFMDC-GTODL method over other compared approaches.

Advancing medical data classification through federated learning and blockchain incentive mechanism: implications for modern software systems and applications

The key issue of medical data is patient information sensitivity and dataset finiteness, which need to guarantee high-efficient training. Besides, the current convolutional neural network has a low image classification and poor robustness concerning antagonistic samples. A lack of scalability in healthcare federated learning and incentive mechanism hinders the attraction of ample high-quality datasets. This paper proposes a Federated Learning Incentive Mechanism for Medical Data Classification (FedIn-MC). It realizes a collaborative model training of multi-party medical institutions through the combination of federated learning and blockchain. There is a marked improvement to the model’s robustness through a combination of the distance loss function and the prototype loss regulation. In addition, this incentive mechanism of blockchain in the project is applied to calculate client contribution values and encourage healthcare institutions to active training model participation. Simulation results verify an accomplishment of a multi-party training. With regard to image classifications, this framework also has a higher classification accuracy and stronger robustness concerning invisible class samples.

Algorithm for Classification of Patient Groups Based on the LDA Method

Introduction. The article is devoted to the development of the algorithm of classification of several groups of patients using the LDA method at certain stages of its work. The algorithm allows achieve high discrimination value, to work with a variety of parameters and to separate more than two groups of patients. It is used for the analysis of biomedical data, namely for the array of indicators of various nature described healthy persons and persons with the detected disease. The purpose of paper is to develop an algorithm to increase the power of discrimination of an array of medical data that may include hundreds of healthy people and patients with connective tissue diseases, and each person is described by hundreds of parameters. Results. The authors proposed an algorithm for increasing classification accuracy based on parameter selection using sequential multivariate LDA tests. An example of the application of the algorithm to the task of analyzing a fairly large array of data (501 individuals: 295 healthy, 206 patients, 240 parameters) and identifying informative parameters for diagnosing children with connective tissue diseases is given. As a result of the application of this algorithm, a discriminant function and a decision rule were obtained, which allows achieve an average accuracy of discrimination over the entire set of parameters of 85 %. In addition, the algorithm is applied for two separate groups of parameters - blood indicators and biochemical analysis, while the average accuracy of discrimination reaches 84 % and 90 %, respectively. Conclusions. The algorithm of classification groups of patients with the use of LDA has been developed, which allows achieve high accuracy of discrimination. The results of its application in solving the real data set are given. The results of its application to solving the task of classification of medical data show its ability to improve classification accuracy. Keywords: algorithm, classification, analysis, informative parameters, LDA.

Hybrid filtering methods for feature selection in high-dimensional cancer data

<span lang="EN-US">Statisticians in both academia and industry have encountered problems with high-dimensional data. The rapid feature increase has caused the feature count to outstrip the instance count. There are several established methods when selecting features <span>from massive amounts of breast cancer data. Even so, overfitting continues to be a problem. The challenge of choosing important features with minimum loss in a different sample size is another area with room for development. As a result, the feature selection technique is crucial for dealing with high-dimensional</span> data classification issues. This paper proposed a new architecture for high-dimensional breast cancer data using filtering techniques and a logistic regression model. Essential features are filtered out using a combination of hybrid chi–square and hybrid information gain (hybrid IG) with logistic regression as classifier. The results showed that hybrid IG performed the best for high-dimensional breast and prostate cancer data. The top 50 and 22 features outperformed the other configurations, with the highest classification accuracies of 86.96% and 82.61%, respectively, after integrating the hybrid <span>information gain and logistic function (hybrid IG+LR) with a sample size of 75. In the future, multiclass classification of multidimensional medical data to be evaluated using data from</span> a different domain<span>.</span></span>

Volumetric Model Genesis in Medical Domain for the Analysis of Multimodality 2-D/3-D Data Based on the Aggregation of Multilevel Features

The automatic and accurate classification of medical imaging data has potential applications in computer-aided disease diagnosis, prognosis, and treatment. However, it remains a challenge to optimize recent deep learning algorithms in medical domain for the accurate classification of large-scale 3D volumetric data. To address these challenges, we propose an efficient deep volumetric classification network based on the aggregation of multilevel deep features for accurate classification of large-scale medical 2D/3D imaging data. To perform a detailed quantitative analysis of our method, 26 different datasets were fused to construct a single large-scale multimodal database that comprises a total of seventy different classes, including 151,095 data samples. Additionally, 15 different baseline methods were configured under the same experimental protocol for volumetric model genesis and extensive performance comparison with our method. The experimental results of our method exhibited promising performance as area under the curve of 93.66% and outperformed various state-of-the-art methods.

Data Pre-processing Issues in Medical Data Classification

With digitalization of data and the rise of World Wide Web, access to information has been very easy and affordable. Especially the Web and the Internet have boosted research activities by facilitating access to large, publicly available medical datasets under open access scheme. These developments have resulted in explosive amounts of data being generated varying in volume, variety and velocity thus referred to as big data. Availability of such medical big data has catalyzed the research in medical predictive analytics. However, the true value of such data can be derived only after subjecting it to careful processing and analysis before drawing inferences from it. Publicly available medical datasets have noise in the form of missing values, outliers and data inconsistencies, that may affect the results or outcomes negatively. Pre-processing of such data is essential to eliminate noisy elements and refine the data to be suitable for further analysis and processing. This paper signifies the need for data pre-processing and explains the data pre-processing pipeline with various underlying stages constituting it. It also presents a comparative analysis of various data pre-processing techniques for handling missing values and outliers in a dataset..

PCA based feature extraction and MPSO based feature selection for gene expression microarray medical data classification

In this paper, a novel Multi Class based Feature Extraction (MC-FE) method has been proposed for medical data classification. Genomic datasets, or gene expression-based microarray medical datasets, are categorised for cancer diagnosis. The first stage involves applying a feature extraction technique. The Principal Component Analysis (PCA) is used to extract the features for medical data classification to detect leukemia, colon tumors, and prostate cancer. The MPSO (modified particle swarm optimization) technique is used at the second stage to pick features from high-dimensional microarray medical datasets like prostate cancer, leukemia, and colon tumors. Finally, SVM, KNN, and Naive Bayes classifiers are used to classify medical data. Then, the classified data are stored in the cloud IOT sensors. When compared to the existing methods, the proposed method gives better optimized results for SVM, KNN, and Naive Bayes of 0.88, 0.86, and 0.73, respectively. The results of cancer data extraction and feature selection are also contrasted and assessed using certain performance measure factors.

Heart disease prediction (HDP) using Artificial Intelligence and IoMT for intelligent healthcare models

The present growth in Internet of Medical Things (IoMT) and Artificial Intelligence (AI) paved a way for advanced healthcare systems from conventional methods. The integration of AI and IoMT provides varied chances in medical domain. With that concern, the proposed model derives a novel model for Heart Disease Prediction (HDP), incorporates IoMT and AI. The proposed model comprises of different phases of functions, as, data collection, data preparation, feature optimization and selection, classification. IoMT devices include medical or wearable sensors are used for continuous collection of medical statistics while machine learning model process the data for disease prediction. Here, a new feature selection model called Enhanced Binary Particle Swarm Optimization (EBPSO) for reducing joint feature selection problems. With the extracted features, classification is performed with Cascaded Long Short Term Memory (CLSTM) model for attaining better accuracy of medical data classification. During evaluation, the proposed HDP model achieved the maximal accuracy in disease prediction. Hence, the model can be effectively used for diagnosing heart disease in Smart Healthcare Models.