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Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment

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SCIENTIFIC REPORTS 2025年第1期15卷 1-15页

作者： Rehman, Moiz Ur Naseem, Shahid Butt, Ateeq Ur Rehman Mahmood, Tariq Khan, Amjad Rehman Khan, Inayat Khan, Jawad Jung, Younhyun Univ Educ Fac Informat Sci Div Sci & Technol Township Campus Lahore 54770 Pakistan Natl Text Univ Dept Comp Sci Faisalabad 37610 Pakistan Prince Sultan Univ CCIS Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Engn & Technol Mardan Dept Comp Sci Mardan 23200 Pakistan Gachon Univ Sch Comp Seongnam 13120 South Korea Univ Educ Dept Comp Sci Vehari Campus Vehari 61161 Pakistan

Worldwide, coronary heart disease (CHD) is a leading cause of mortality, and its early prediction remains a critical challenge in clinical data analysis. Machine learning (ML) offers valuable diagnostic support by leveraging healthcare data to enhance decision-making and prediction accuracy. Although numerous studies have applied ML classifiers for heart disease prediction, their contributions often lack clarity in addressing key challenges. In this paper, we present a comprehensive ML framework that systematically tackles these issues. First, we employ mutual information (MI) for effective feature selection to isolate the most informative predictors. Second, we address the significant class imbalance in the dataset using the Synthetic Minority Oversampling Technique (SMOTE), which substantially improves model training. Third, we propose a novel hybrid model that integrates particle swarm optimization (PSO) with an artificial neural network (ANN) to optimize feature weighting and bias training. Additionally, we conduct a comparative analysis with traditional classifiers, including Logistic Regression and Random Forest, using the National Health and Nutritional Examination Survey dataset. Our results demonstrate that while conventional classifiers achieve an accuracy of 95.8%, the proposed PSO-ANN model attains an enhanced accuracy of up to 97% in predicting CHD. This work clearly defines its contributions by improving feature selection, handling data imbalance, and introducing an innovative hybrid model for superior prediction performance.

关键词： Coronary Heart Disease Machine Learning Mutual Information Particle Swarm Optimization artificial Neural Network Health Informatics Medical Images Health Issues

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MVLA-Net:A Multi-View Lesion Attention Network for Advanced Diagnosis and Grading of Diabetic Retinopathy

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Computers, Materials & Continua 2025年第4期83卷 1173-1193页

作者： Tariq Mahmood Tanzila Saba Faten S.Alamri Alishba Tahir Noor Ayesha Artificial Intelligence and Data Analytics(AIDA)Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Shifa College of Medicine Shifa Tameer-E-Millat UniversityIslamabad44000Pakistan Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic *** traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic *** the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior ***,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale *** paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based *** proposed model aims to improve comprehensive segmentation for retinal images with varying vessel *** employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency *** that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy *** evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental *** models achieved approximately 85%accuracy with the same image preparation ***,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,*** results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurat

关键词： Diabetic retinopathy grading retinal vessel segmentation dual-modal deep learning attention mecha-nism health risks

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Skin cancer detection using dermoscopic images with convolutional neural network

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SCIENTIFIC REPORTS 2025年第1期15卷 1-31页

作者： Nawaz, Khadija Zanib, Atika Shabir, Iqra Li, Jianqiang Wang, Yu Mahmood, Tariq Rehman, Amjad Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Univ Educ Dept Comp Sci Vehari Campus Vehari 61161 Pakistan Beijing Univ Technol Beijing Engn Res Ctr IoT Software & Syst Beijing 100124 Peoples R China Shandong Res Inst Ind Technol Jinan Shandong Peoples R China Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Lahore 54000 Pakistan

Skin malignant melanoma is a high-risk tumor with low incidence but high mortality rates. Early detection and treatment are crucial for a cure. Machine learning studies have focused on classifying melanoma tumors, but these methods are cumbersome and fail to extract deeper features. This limits their ability to distinguish subtle variations in skin lesions accurately, hindering effective early diagnosis. The study introduces a deep learning-based network specifically designed for skin lesion detection to enhance data in the melanoma dataset. It leverages a novel FCDS-CNN architecture to address class-imbalanced problems and improve data quality. Specifically, FCDS-CNN incorporates data augmentation and class weighting techniques to mitigate the impact of imbalanced classes. It also presents a practical, large-scale solution that allows seamless, real-world incorporation to support dermatologists in their early screening processes. The proposed robust model incorporates data augmentation and class weighting to improve performance across all lesions. The proposed dataset includes 10015 images of seven classes of skin lesions available in Kaggle. To overcome the dominance of one class over the other, methods like data augmentation and class weighting are used. The FCDS-CNN showed improved accuracy with an average accuracy of 96%, outperforming pre-trained models such as ResNet, EfficientNet, Inception, and MobileNet in the precision, recall, F1-score, and area under the curve parameters. These pre-trained models are more effective for general image classification and struggle with the nuanced features and class imbalances inherent in medical image datasets. The FCDS-CNN demonstrated practical effectiveness by outperforming the compared pre-trained model based on distinct parameters. This work is a testament to the importance of specificity in medical image analysis regarding skin cancer detection.

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Leveraging CNN-Transformer Hybrid Architecture for Accurate Breast Tumor Segmentation in Mammograms

Leveraging CNN-Transformer Hybrid Architecture for Accurate ...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Mudassar Ali Muhammad Zeeshan Tahir Iqra Mariam Khadija Nawaz Tariq Mahmood Samia Allaoua Chelloug College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Computer Science and Technology Zhejiang University Hangzhou China School of Electronics and Information Engineering Liaoning University of Technology Jinzhou China Faculty of Information Sciences University of Education Vehari Pakistan Artificial Intelligence and Data Analytics Lab CCIS PSU Riyadh SA Department of Information Technology CCIS PNU Riyadh SA

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Breast cancer detection through mammograms is very crucial for early diagnosis, and tumor segmentation is an important step in treatment planning. This paper proposes a novel approach by combining CNNs with transformers, which we call the Hybrid CNN + Transformer model, for the task of breast tumor segmentation. It leverages the strengths of CNN s in local feature extraction to capture fine details, such as tumor boundaries, and combines them with transformers that model long-range dependencies and contextual relationships across images for the exact delineation of tumor regions in complex tissue structures. Our methodology will be tested on the high-quality mammog-raphy dataset annotated with both benign and malignant lesions, the so-called INbreast dataset. Herein, the CNN backbone-first for instance, ResNet or EfficientNet-extracts the input mammo-gram hierarchically; features are fed into a transformer layer in order to capture global dependencies, after which the final segmentation mask is generated by using a U-Net style decoder for a pixel-wise tumor classification. The segmentation accuracies of our models demonstrated the significant improvement achieved in tumor segmentation, using Hybrid CNN + Transformer when compared with tra-ditional CNN-based models on challenging tumor boundaries and complex tissue patterns. An accurate, robust, completely automatic method for tumor segmentation will thus enable improved diagnostic feasibility on clinical grounds. Furthermore, the combination of learning at both local and global features improved generalizability to previously unseen mammogram images and therefore strengthened clinical applicability.

关键词： Image segmentation Adaptation models Accuracy Breast tumors Computational modeling Transformers Mammography Breast cancer Convolutional neural networks Tumors

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Noise filtering approach to improve handwritten digit recognition using customized CNN for Cerebral Palsy individuals

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The European Physical Journal Special Topics 2025年 1-19页

作者： Muthureka, K. Srinivasulu Reddy, U. Janet, B. Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology-Tiruchirappalli Tiruchirappalli India Centre of Excellence (CoE) in Artificial Intelligence National Institute of Technology-Tiruchirappalli Tiruchirappalli India Information Processing Lab Department of Computer Applications Tiruchirappalli India

Automatic recognition of handwritten digits in people with Cerebral Palsy (CP) is a serious challenge that requires advancements in data preparation for improved predictive accuracy. The disorganized digit pixel patterns caused by CP people’ innate motor coordination problems result in the classification errors due to the noise and outlier. Despite the fact that many handwritten digit databases for normal people are available, none expressly cater to those with shaky hands/CP dataset. This highlights the importance of an efficient pre-processing method for mitigating variances in CP individuals’ digit pixel patterns. A total of 43,000 images from 43 CP people were collected and organized into 10 different folders for data labeling. The authors used the K-Means Based Noise Removal (KMNR) approach to remove noise and outliers from a CP and tested it on a customized CNN. The KMNR model achieved an accuracy of 98.86%, compared to 93.54% without it. The F1-score values for state-of-the-art models without KMNR ranged from 85.00% to 97.00%, but after integrating KMNR, they increased to 92.00% to 98.00%. The precision values ranged from 85.00% to 97.00% in the absence of KMNR, but increased to 91.00% to 98.00% in the presence of KMNR. The recall values ranged from 85.00% to 97.00% without KMNR, but increased to 87.00% to 98.00% with KMNR.

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Leveraging Deep Learning for Cyberbullying Detection in Arabic Tweets

Leveraging Deep Learning for Cyberbullying Detection in Arab...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Fatima AlShannaq Sawsan Alhalawani Heba Al-Jarrah Mohammad Shehab Artificial Intelligence and Data Analytics Lab (AIDA) CCIS Prince Sultan University Riyadh Saudi Arabia College of Computer Science and Information Systems Prince Sultan University Riyadh Saudi Arabia Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan College of Computer Sciences and Informatics Amman Arab University Amman Jordan

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Cyberbullying is a growing concern in the digital age, particularly on social media platforms like X (formerly Twitter). This study presents a deep learning-based approach for detecting cyberbullying in Arabic tweets using the Arabic Cyberbullying Corpus (ArCybC). Leveraging the Arabic-BERT model, we conducted binary and multi-class classification tasks to identify cyberbullying content and its associated categories or domains. Our methodology incorporated advanced data-balancing techniques, including oversampling and undersam-pling, to address class imbalance challenges. The study aims to answer the research questions: (1) How effectively can deep learning models detect cyberbullying in Arabic tweets? (2) How do data balancing techniques impact classification performance? (3) Can Arabic-BERT outperform traditional classifiers in cy-berbullying detection? These questions are addressed through our methodology, where we employ Arabic-BERT, implement rebalancing techniques, and compare our model's performance with baseline classifiers. Experimental results demonstrate significant improvements in F1-scores, with 89.1% achieved in binary classification and 91.6% in category-level classification after oversampling. These findings highlight the effectiveness of fine-tuned deep learning models in tackling the linguistic complexity of Arabic and the nuanced nature of cyberbullying detection. This study provides a foundation for future research in addressing online harassment in Arabic-speaking digital communities.

关键词： Deep learning Blogs Cyberbullying Linguistics data science Information age data models Complexity theory

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Correction: MSegNet: A Multi-View Coupled Cross-Modal Attention Model for Enhanced MRI Brain Tumor Segmentation

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International Journal of Computational intelligence Systems 2025年第1期18卷 1-2页

作者： Wang, Yu Xu, Juan Guan, Yucheng Ahmad, Faizan Mahmood, Tariq Rehman, Amjad Shandong Research Institute of Industrial Technology Shandong China Department of Endocrinology Taian City Central Hospital Taian China College of Engineering Northeastern University San Jose USA Qiqihar Medical University Heilongjiang Province China Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh Saudi Arabia Faculty of Information Sciences University of Education Vehari Campus Vehari Pakistan

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data Frequency Coverage Impact on AI Performance

Data Frequency Coverage Impact on AI Performance

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Erin Lanus Brian Lee Jaganmohan Chandrasekaran Laura J. Freeman M S Raunak Raghu N. Kacker D. Richard Kuhn National Security Institute Virginia Tech Arlington VA USA Sanghani Center for Artificial Intelligence & Data Analytics Virginia Tech Arlington VA USA Information Technology Lab National Institute of Standards and Technology MD USA

ISBN: (数字)9798331534677

ISBN: (纸本)9798331534684

artificial intelligence (AI) models use statistical learning over data to solve complex problems for which straightforward rules or algorithms may be difficult or impossible to design; however, a side effect is that models that are complex enough to sufficiently represent the function may be uninterpretable. Combinatorial testing, a black-box approach arising from software testing, has been applied to test AI models. A key differentiator between traditional software and AI is that many traditional software faults are deterministic, requiring a failureinducing combination of inputs to appear only once in the test set for it to be discovered. On the other hand, AI models learn statistically by reinforcing weights through repeated appearances in the training dataset, and the frequency of input combinations plays a significant role in influencing the model’s behavior. Thus, a single occurrence of a combination of feature values may not be sufficient to influence the model’s behavior. Consequently, measures like simple combinatorial coverage that are applicable to software testing do not capture the frequency with which interactions are covered in the AI model’s input space. This work develops methods to characterize the data frequency coverage of feature interactions in training datasets and analyze the impact of imbalance, or skew, in the combinatorial frequency coverage of the training data on model performance. We demonstrate our methods with experiments on an open-source dataset using several classical machine learning algorithms. This pilot study makes three observations: performance may increase or decrease with data skew, feature importance methods do not predict skew impact, and adding more data may not mitigate skew effects.

关键词： Training Hands Machine learning algorithms Statistical learning Software algorithms Training data data models Frequency measurement Software measurement artificial intelligence

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Understanding Cross-Model Perceptual Invariances Through Ensemble Metamers

arXiv

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arXiv 2025年

作者： Lukas, Lukas Mueller, Jonas Leo Loeffler, Christoffer Schwinn, Leo Eskofier, Bjoern Zanca, Dario Machine Learning and Data Analytics Lab Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander University Erlangen Germany School of Informatics Pontificia Universidad Católica de Valparaíso Valparaiso Chile Data Analytics and Machine Learning Lab Technische Universität München Munich Germany Institute of AI for Health Helmholtz Zentrum München Munich Germany

Understanding the perceptual invariances of artificial neural networks is essential for improving explainability and aligning models with human vision. Metamers—stimuli that are physically distinct yet produce identical neural activations—serve as a valuable tool for investigating these invariances. We introduce a novel approach to metamer generation by leveraging ensembles of artificial neural networks, capturing shared representational subspaces across diverse architectures, including convolutional neural networks and vision transformers. To characterize the properties of the generated metamers, we employ a suite of image-based metrics that assess factors such as semantic fidelity and naturalness. Our findings show that convolutional neural networks generate more recognizable and human-like metamers, while vision transformers produce realistic but less transferable metamers, highlighting the impact of architectural biases on representational invariances. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Combining stochastic tendency and distribution overlap towards improved nonparametric effect measures and inference

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Scandinavian Journal of Statistics 2025年第0期

作者： Beck, Jonas Langthaler, Patrick B. Bathke, Arne C. Intelligent Data Analytics (IDA) Lab Department of Artificial Intelligence and Human Interfaces (AIHI) Paris Lodron University of Salzburg Salzburg Austria Department of Neurology Paracelsus Medical University of Salzburg Salzburg Austria

A fundamental functional in nonparametric statistics is the Mann-Whitney functional (Formula presented.), which constitutes the basis for the most popular nonparametric procedures. The functional (Formula presented.) measures a location or stochastic tendency effect between two distributions. A limitation of (Formula presented.) is its inability to capture scale differences. If differences of this nature are to be detected, specific tests for scale or omnibus tests need to be employed. However, the latter often suffer from low power, and they do not yield interpretable effect measures. In this article, we extend (Formula presented.) by additionally incorporating the recently introduced distribution overlap index (nonparametric dispersion measure) (Formula presented.) that can be expressed in terms of the quantile process. We derive the joint asymptotic distribution of the respective estimators of (Formula presented.) and (Formula presented.) and construct confidence regions. Extending the Wilcoxon–Mann–Whitney test, we introduce a new test based on the joint use of these functionals. It results in much larger consistency regions while maintaining competitive power to the rank sum test for situations in which (Formula presented.) alone would suffice. Compared with classical omnibus tests, the simulated power is much improved. Additionally, the newly proposed inference method yields effect measures whose interpretation is surprisingly straightforward. © 2025 The Author(s). Scandinavian Journal of Statistics published by John Wiley & Sons Ltd on behalf of The Board of the Foundation of the Scandinavian Journal of Statistics.

关键词： confidence region nonparametric test resampling simultaneous inference

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