MapReduce Based Big Data Framework Using Associative Kruskal Poly Kernel Classifier for Diabetic Disease Prediction

MethodsX, Journal Year: 2025, Volume and Issue: 14, P. 103210 - 103210

Published: Feb. 6, 2025

Recent trendy applications of Artificial Intelligence are Machine Learning (ML) algorithms, which have been extensively utilized for processes like pattern recognition, object classification, effective prediction disease etc. However, ML techniques reasonable solutions to computation methods and modeling, especially when the data size is enormous. These facts established due reason that big field has received considerable attention from both industrial experts academicians. The process must be accelerated achieve early in order accomplish prospects applications. In this paper, a method named "Associative Kruskal Wallis MapReduce Poly Kernel (AKW-MRPK)" presented prediction. Initially, significant attributes selected by applying Associative Feature Selection model. This study parallelizes polynomial kernel vector using based on qualities gained, will become computing model facilitate prognosis disease. proposed AKW-MRPK framework achieves up 92 % accuracy, reduces computational time as low 0.875 ms 25 patients, demonstrates superior speedup efficiency with value 1.9 two nodes, consistently outperforming supervised machine learning algorithms Hadoop-based clusters across these critical metrics.•The selects accelerates computations predictions.•Parallelizing kernels improves accuracy speed healthcare analysis.

Language: Английский

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Synergistic Feature Selection and Distributed Classification Framework for High-Dimensional Medical Data Analysis

MethodsX, Journal Year: 2025, Volume and Issue: 14, P. 103219 - 103219

Published: Feb. 13, 2025

Feature selection and classification efficiency accuracy are key to improving decision-making regarding medical data analysis. Since the datasets large complex, they give rise certain problematic issues such as computational complexity, limited memory space, a lesser number of correct classifications. In order overcome these drawbacks, new integrated algorithm is presented here: Synergistic Kruskal-RFE Selector Distributed Multi-Kernel Classification Framework (SKR-DMKCF). The innovative architecture SKR-DMKCF results in reduction dimensionality while preserving useful characteristics image utilizing recursive feature elimination multi-kernel distributed environment. Detailed evaluations were performed on four broad established our performance advantage. average ratio was 89 % for proposed method, SKR-DMKCF, which can outperform all methods by achieving best 85.3 %, precision 81.5 recall 84.7 %. On calculations, it seen that usage 25 compared existing speed-up time significant improvement well assure scalability resource-limited environments.•Innovative efficient datasets.•Distributed superior efficiency.

Language: Английский

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Advanced Image Preprocessing and Context-Aware Spatial Decomposition for Enhanced Breast Cancer Segmentation

MethodsX, Journal Year: 2025, Volume and Issue: 14, P. 103224 - 103224

Published: Feb. 15, 2025

Language: Английский

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Integrated normal discriminant analysis in mapreduce for diabetic chronic disease prediction using bivariant deep neural networks

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(8), P. 4915 - 4929

Published: Aug. 11, 2024

Language: Английский

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Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis

MethodsX, Journal Year: 2025, Volume and Issue: unknown, P. 103276 - 103276

Published: March 1, 2025

The task of predicting liver tumors is critical as part medical image analysis and genomics area since diagnosis prognosis are important in making correct decisions. Silent characteristics interactions between genomic imaging features also the main sources challenges toward reliable predictions. To overcome these hurdles, this study presents two integrated approaches namely, - Attention-Guided Convolutional Neural Networks (AG-CNNs), Genomic Feature Analysis Module (GFAM). Spatial channel attention mechanisms AG-CNN enable accurate tumor segmentation from CT images while providing detailed morphological profiling. Evaluation with three control databases TCIA, LiTS, CRLM shows that our model produces more output than relevant literature an accuracy 94.5%, a Dice Similarity Coefficient 91.9%, F1-Score 96.2% for Dataset 3. More considerably, proposed methods outperform all other different datasets terms recall, precision, Specificity by up to 10 percent including CELM, CAGS, DM-ML, so on.•Utilization (AG-CNN) enhances region focus accuracy.•Integration (GFAM) identifies molecular markers subtype-specific classification.

Language: Английский

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Multi-Scale Cascaded Spatial Segmentation Transformer for Liver Cancer Classification

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Early and accurate detection is crucial for treating liver cancer, the main cause of cancer deaths. Despite its widespread use, Computed Tomography (CT) imaging generally struggles with tumors' low contrast, uneven borders, overlapping features. The variety in tumor forms, sizes, complicated anatomical aspects makes CT image segmentation categorization difficult. Variability size shape, structures, complex anatomy are some difficulties that this method aims to address when using images diagnose cancer. Multi-Scale Cascaded Spatial Segmentation Transformer (M-SCSST) an innovative approach developed Classification Liver Cancer from Images introduced research. M-SCSST uses a cascaded processing include multi-scale spatial information into transformer-based architecture. Accurate classification heterogeneous cancers made possible by enhancing subtle features utilizing advanced attention mechanisms (AAM). Improved diagnostic accuracy achieved employing suggested on large dataset Its use helps radiologists identify cancerous benign areas, which leads earlier diagnosis better treatment choices. effectiveness scans assessed through comprehensive simulation Research measures precision, recall, computational efficiency, noise resilience, accuracy. With improved reliability, detects more effectively than conventional approaches.

Language: Английский

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AI-Driven Preclinical Advances in Nuclear Medicine Radiopharmaceutical Therapy for Prostate Cancer

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 203 - 230

Published: Feb. 28, 2025

Radiopharmaceutical therapy represents a growing treatment modality in prostate cancer, and this chapter will focus on how Artificial Intelligence (AI) can be applied to preclinical development of paradigm. The study, presents Multi-Modal Attention Enhanced Neural Network (MAENN) integrate disparate data types—imaging, molecular pharmacokinetics—to improve predictions efficacy, determination optimal dose the identification specific biomarkers. Compared number conventional models such as SVMs CNNs, MAENN maintains highest accuracy prediction, sensitivity integration capabilities. Although issues like quality computational complexity remain, also describes scalability, potential for real-time applications, future applications other cancer therapies, contributing towards personalizing strategies order pave way precision oncology.

Language: Английский

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AI-Driven Advancements in Hybrid Imaging for Nuclear Medicine

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 247 - 270

Published: Feb. 28, 2025

The development of AI models in hybrid imaging systems like PET-CT, PET-MRI, SPECT-CT mark the future nuclear medicine. Advances such as deep learning, machine and explainable (XAI) have similarly revolutionized by improving image quality achieving increased efficiency segmenting images well data acquisition reconstruction real time. Such improvements enhance diagnostic certainty, minimize exposure to radiation with low dose scan methods, enable improved identification measurement lesions. Additionally, enables multi-modal fusion, where fMRI structural MRI plus functional MRI, molecular imaging, genomics or clinical all come together progress coup approach personalization This chapter discusses how plays a significant role extent highlight on its current usage, elucidate challenges that are noted exist prospects

Language: Английский

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Optimized Disease Prediction in Healthcare Systems Using HDBN and CAEN Framework

MethodsX, Journal Year: 2025, Volume and Issue: 14, P. 103338 - 103338

Published: April 25, 2025

Classification and segmentation play a pivotal role in transforming decision-making processes healthcare, IoT, edge computing. However, existing methodologies often struggle with accuracy, precision, specificity when applied to large, heterogeneous datasets, particularly minimizing false positives negatives. To address these challenges, we propose robust hybrid framework comprising three key phases: feature extraction using Hybrid Deep Belief Network (HDBN), dynamic prediction aggregation via Custom Adaptive Ensemble (CAEN), an optimization mechanism ensuring adaptability robustness. Extensive evaluations on four diverse datasets demonstrate the framework's superior performance, achieving 93 % 87 95 specificity, 91 recall. Advanced metrics, including Matthews Correlation Coefficient of 0.8932, validate its reliability. The proposed establishes new benchmark for scalable, high-performance classification segmentation, offering solutions real-world applications paving way future integration explainable AI real-time systems.•Designed novel integrating HDBN CAEN adaptive prediction.•Proposed strategies enhancing robustness across data scenarios.

Language: Английский

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Dual-Phase Regressive Deep Neural MapReduce Classifier for Scalable and Accurate Diabetic Prediction

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(5)

Published: May 5, 2025

Language: Английский

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