A Hybrid Transfer Learning Approach Using Obesity Data for Predicting Cardiovascular Diseases Incorporating Lifestyle Factors DOI Creative Commons
Krishna Modi, Ishbir Singh, Yogesh Kumar

et al.

International Journal of experimental research and review, Journal Year: 2024, Volume and Issue: 46, P. 1 - 18

Published: Dec. 30, 2024

Cardiovascular Diseases (CVDs), particularly heart diseases, are becoming a significant global public health concern. This study enhances CVD detection through novel approach that integrates obesity prediction using machine learning (ML) models. Specifically, model trained on an dataset was used to add 'Obesity level' feature the disease dataset, leveraging relation of high with increased risk. We have also calculated BMI and added as in dataset. evaluated this transfer learning-based alongside eight ML Performance these models assessed precision, recall, accuracy F1-score metrics. Our research aims provide healthcare practitioners reliable tools for early diagnosis. Results indicate ensemble methods, which combine strengths multiple models, significantly improve compared other classifiers. able achieve 74% score along 0.72 F1 score, 0.77 precision 0.80 AUC XGBoost classifier, followed closely by DNN 73.7% 0.75 0.798 our proposed model. seek enhance efficiency promote integrating AI-based solutions into medical practice. The findings demonstrate potential techniques effectiveness incorporating obesity-related features optimized cardiovascular detection.

Language: Английский

HeartEnsembleNet: An Innovative Hybrid Ensemble Learning Approach for Cardiovascular Risk Prediction DOI Open Access
Syed Ali Jafar Zaidi,

Abdul Ghafoor,

Jun Kim

et al.

Healthcare, Journal Year: 2025, Volume and Issue: 13(5), P. 507 - 507

Published: Feb. 26, 2025

Cardiovascular disease (CVD) is a prominent determinant of mortality, accounting for 17 million lives lost across the globe each year. This underscores its severity as critical health issue. Extensive research has been undertaken to refine forecasting CVD in patients using various supervised, unsupervised, and deep learning approaches. study presents HeartEnsembleNet, novel hybrid ensemble model that integrates multiple machine (ML) classifiers risk assessment. The evaluated against six classical ML classifiers, including support vector (SVM), gradient boosting (GB), decision tree (DT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF). Additionally, we compare HeartEnsembleNet with Hybrid Random Forest Linear Models (HRFLM) techniques stacking voting. Employing dataset 70,000 cardiac 12 clinical attributes, our proposed achieves notable accuracy 92.95% precision 93.08%. These results highlight effectiveness enhancing prediction, offering promising framework support.

Language: Английский

Citations

3
Integration of Artificial Intelligence and Advanced Optimization Techniques for Continuous Gas Lift under Restricted Gas Supply: A Case Study DOI Creative Commons

Leila Zeinolabedini,

Forough Ameli, Abdolhossein Hemmati‐Sarapardeh

et al.

Digital Chemical Engineering, Journal Year: 2025, Volume and Issue: 14, P. 100220 - 100220

Published: Feb. 1, 2025

Language: Английский

Citations

0
Predictive Machine Learning Approaches for Supply and Manufacturing Processes Planning in Mass-Customization Products DOI Creative Commons

Shereen Alfayoumi,

Amal Elgammal, Neamat El-Tazi

et al.

Informatics, Journal Year: 2025, Volume and Issue: 12(1), P. 22 - 22

Published: Feb. 19, 2025

Planning in mass-customization supply and manufacturing processes is a complex process that requires continuous planning optimization to minimize time cost across wide variety of choices large production volumes. While soft computing techniques are widely used for optimizing products, they face scalability issues when handling datasets rely heavily on manually defined rules, which prone errors. In contrast, machine learning offer an opportunity overcome these challenges by automating rule generation improving scalability. However, their full potential has yet be explored. This article proposes learning-based approach address this challenge, aiming optimize both the phases as practical solution industry or problems. The proposed examines supervised deep various scenarios large-scale real-life pilot study bicycle domain. experimentation included K-Nearest Neighbors with regression Random Forest from family, well Neural Networks Ensembles approaches. Additionally, Reinforcement Learning was where real-world data historical experiences were unavailable. training performance evaluated using cross-validation along two statistical analysis methods: t-test Wilcoxon test. These evaluation efforts revealed outperform methods reinforcement approach, K-NN combined yielding best results. validated experts manufacturing. It demonstrated up 37% reduction orders compared traditional expert estimates.

Language: Английский

Citations

0
Finite-Time Cluster Synchronization of Fractional-Order Complex-Valued Neural Networks Based on Memristor with Optimized Control Parameters DOI Creative Commons
Qi Chang, Rui Wang,

Yongqing Yang

et al.

Fractal and Fractional, Journal Year: 2025, Volume and Issue: 9(1), P. 39 - 39

Published: Jan. 14, 2025

The finite-time cluster synchronization (FTCS) of fractional-order complex-valued (FOCV) neural network has attracted wide attention. It is inconvenient and difficult to decompose networks into real parts imaginary parts. This paper addresses the FTCS coupled memristive (CMNNs), which are FOCV systems with a time delay. A controller designed sign function achieve using non-decomposition approach, eliminates need separate system its components. By applying stability theory, some conditions derived for based on proposed controller. settling time, related system’s initial values, can be computed Mittag–Leffler function. We further investigate optimization control parameters by formulating an model, solved particle swarm (PSO) determine optimal parameters. Finally, numerical example comparative experiment both provided verify theoretical results method.

Language: Английский

Citations

0
Enhancing Heart Disease Diagnosis with Meta-Heuristic Algorithms: A Combined HHO and PSO Approach DOI
Farzana Begum,

J. Arul Valan

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 21, 2025

Language: Английский

Citations

0
Artificial Intelligence in Primary Care—Transforming Hong Kong Breast Cancer Screening DOI
Tianqing Chu,

L Lui

Quality of life in Asia, Journal Year: 2025, Volume and Issue: unknown, P. 611 - 624

Published: Jan. 1, 2025

Language: Английский

Citations

0
Maternal Health Risk Detection: Advancing Midwifery with Artificial Intelligence DOI Open Access
Katerina D. Tzimourta, Markos G. Tsipouras, Pantelis Angelidis

et al.

Healthcare, Journal Year: 2025, Volume and Issue: 13(7), P. 833 - 833

Published: April 6, 2025

Background/Objectives: Maternal health risks remain one of the critical challenges in world, contributing much to maternal and infant morbidity mortality, especially most vulnerable populations. In modern era, with recent progress area artificial intelligence machine learning, promise has emerged regard achieving goal early risk detection its management. This research is set out relate high-risk, low-risk, mid-risk using learning algorithms based on physiological data. Materials Methods: The applied dataset contains 1014 instances (i.e., cases) seven attributes variables), namely, Age, SystolicBP, DiastolicBP, BS, BodyTemp, HeartRate, RiskLevel. preprocessed used was then trained tested six classifiers 10-fold cross-validation. Finally, performance metrics models erre compared like Accuracy, Precision, True Positive Rate. Results: best found for Random Forest, also reaching highest values Accuracy (88.03%), TP Rate (88%), Precision (88.10%), showing robustness handling classification. category challenging across all models, characterized by lowered Recall scores, hence underlining class imbalance as bottlenecks performance. Conclusions: Machine hold strong potential improving prediction. findings underline place advancing healthcare driving more data-driven personalized approaches.

Language: Английский

Citations

0
A smart CardioSenseNet framework with advanced data processing models for precise heart disease detection DOI

R. Subathra,

V. Sumathy

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 185, P. 109473 - 109473

Published: Dec. 3, 2024

Language: Английский

Citations

2
Boosting medical diagnostics with a novel gradient-based sample selection method DOI
Samet Aymaz

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 182, P. 109165 - 109165

Published: Sept. 24, 2024

Language: Английский

Citations

0
Epidemiological passport system requirements: a roadmap for international travel and tourism recovery DOI Creative Commons

Omar Mohammad Ali,

Abduladhim Ashtaiwi, Ashraf Jaradat

et al.

Cogent Social Sciences, Journal Year: 2024, Volume and Issue: 10(1)

Published: June 18, 2024

Global tourism demand is vulnerable to pandemics such as the COVID-19 pandemic, which made international travel difficult if not impossible. To improve robustness of global in advent pandemics, this article explores an epidemiological passport system (EPS), reported on article. attain different perspectives regarding use EPS, research used a qualitative method approach. It carried out 32 detailed interviews with executive leaders organizations sectors obtain their views about main requirements for EPS. An EPS could provide traceability better share important information aspects testing, contact tracing and vaccination. This identified new that will help health border control organizations' collaboration. The findings study hold significant practical implications development implementation designed address multifaceted challenges posed by particularly concerning testing vaccination imposed various governments. contributions are pivotal ensuring seamless while maintaining security regulatory compliance.

Language: Английский

Citations

0