Cited by Exposomics and Cardiovascular Diseases: A Scoping Review of Machine Learning Approaches

From Discounts to Delivery: Decoding Customer Care Interactions in Warehousing DOI

Angelo Leogrande

Опубликована: Янв. 1, 2025

The present research has delved deeper into the complex relationship of customer care calls with purchasing behavior in a WM system and developed actionable insights to optimize operations. In this regard, following critical factors have been considered: product attributes-cost, weight, discount-on one hand, delivery performance terms timeliness reliability on other, view understand their impacts satisfaction interactions. Key takeaways are that high volumes reflect operational failure; there is delay or expectation mismatch, hence needs strong process optimization. Also, heavy products, since perceived be reliable, fewer enquiries; lighter, cheap products cause more frequent queries impulsive buying lack information occur. It further identifies as main determinant while delays result heightened discontent rising demands for support. study underlines strategic relevance advanced analytics, machine learning, real-time monitoring finally resolve recurring inefficiencies. This may also good basis which recommendations could made concerning use predictive analytics demand forecasting, effective logistical frameworks, methods service would line product-specific needs. Discounts become two-edged factor: enhancing but threatening brand value when used too frequently. end, strategies discounts should put balance, proactive engagement there, crystal clear communications them, correctly described. given identified how warehouse clears from customers by applying data-driven better efficiency, satisfaction, long-term loyalty. above findings provide comprehensive road map integrate technology customer-centric modern management.

Язык: Английский

Процитировано

Machine Learning-Based Stacking Ensemble Model for Prediction of Heart Disease with Explainable AI and K-Fold Cross-Validation: A Symmetric Approach DOI

Shamsuddin Sultan,

Nadeem Javaid, Nabil Alrajeh

и другие.

Symmetry, Год журнала: 2025, Номер 17(2), С. 185 - 185

Опубликована: Янв. 25, 2025

One of the most complex and prevalent diseases is heart disease (HD). It among main causes death around globe. With changes in lifestyles environment, its prevalence rising rapidly. The prediction early stages crucial, as delays diagnosis can cause serious complications even death. Machine learning (ML) be effective this regard. Many researchers have used different techniques for efficient detection to overcome drawbacks existing models. Several ensemble models also been applied. We proposed a stacking model named NCDG, which uses Naive Bayes, Categorical Boosting, Decision Tree base learners, with Gradient Boosting serving meta-learner classifier. performed preprocessing using factorization method convert string columns into integers. employ Synthetic Minority Oversampling TEchnique (SMOTE) BorderLineSMOTE balancing address issue data class imbalance. Additionally, we implemented hard soft voting classifier compared results model. For Artificial Intelligence-based eXplainability our NCDG model, use SHapley Additive exPlanations (SHAP) technique. outcomes show that suggested performs better than benchmark techniques. experimental achieved highest accuracy, F1-Score, precision recall 0.91, 0.91 respectively, an execution time 653 s. Moreover, utilized K-Fold Cross-Validation validate predicted results. worth mentioning their validation strongly coincide each other proves approach symmetric.

Язык: Английский

Процитировано

IoT-Cloud-Centric Smart Healthcare Monitoring System for Heart Disease Prediction Using a Gated-Controlled Deep Unfolding Network with Crayfish Optimization DOI

Harish Kumar,

Anuradha Taluja,

Romesh Prasad

и другие.

International Journal of Computational Intelligence and Applications, Год журнала: 2025, Номер unknown

Опубликована: Фев. 11, 2025

The rising incidence of heart disease requires effective and robust prediction algorithms, especially in Internet Things (IoT)-cloud-based smart healthcare frameworks. This study presents a novel method for forecasting cardiovascular using superior data preprocessing, feature selection, deep learning techniques. First, preprocessing is done the Z-score min–max normalization technique to ensure consistent scaling standardize dataset. After an innovative hybrid selection that combines Black Widow Optimization (BWO) Influencer Buddy (IBO) utilized. By achieving equilibrium between invention execution, BWO-IBO enhances extracts most pertinent information prediction. Gates-Controlled Deep Unfolding Network (GCDUN), which based on Crayfish Algorithm (COA), framework Through use gates-controlled mechanism COA component speeds up network parameter tuning crayfish behavior, GCDUN-COA increases representation decision plane. fusion IoT cloud-based takes present collection, processing, remote monitoring notch higher, thus making system highly scalable efficient clinical use. When predicting cardiac disease, recommended shows improved F1-score, specificity, accuracy, recall, precision continuously above 99% across all performance metrics. providing prompt diagnosis intervention via intelligent, adaptive system, IoT-driven medical technology has potential revolutionize care.

Язык: Английский

Процитировано

Hybrid Deep-CNN and Bi-LSTM Model with Attention Mechanism for Enhanced ECG-Based Heart Disease Diagnosis DOI

Kumar Gaurav, Neeraj Varshney

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

Abstract According to the World Health Organization (WHO), 17.9 million people die yearly from cardiovascular Diseases (CVDs), including heart attacks. Cardiovascular diseases, attack, kill 32% of globally. Current approaches struggle with electrocardiogram (ECG) signal variability, causing diagnosing errors. The adoption automated and accurate models for disease detection is lacking since conventional methods rely on human analysis, which time-consuming error-prone. This work covers crucial topic diagnosis, especially ECG data analysis detection. integration Deep-Convolutional Neural Network (Deep-CNN) Bidirectional Long Short-Term Memory (Bi-LSTM) model an Attention Mechanism enhances accuracy reliability categorisation. Deep-CNN component efficiently extracts features capture spatial linkages, while Bi-LSTM layers handle temporal dependencies identify patient health patterns over time. evaluated 303 records 14 clinical characteristics University California, Irvine (UCI) Cleveland Heart Disease dataset. suggested technique has 97.23% accuracy, 97.72% recall, precision, 96.90% F1 score. These findings show that proposed architecture improves diagnostic performance more than boosting ensemble hybrid models.

Язык: Английский

Процитировано

Heart disease detection using novel ensemble approach: RF- GB-SVM stacking classifier DOI

M. S.,

N. Harshini,

J. Felicia Lilian

и другие.

Procedia Computer Science, Год журнала: 2025, Номер 258, С. 2647 - 2658

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

Optimized convolutional neural network using grasshopper optimization technique for enhanced heart disease prediction DOI

Sanjeeva Polepaka, R. P. Ram Kumar,

Deepthi Palakurthy

и другие.

Cogent Engineering, Год журнала: 2024, Номер 11(1)

Опубликована: Ноя. 8, 2024

According to the World Health Organization (WHO), heart disease (HD) is a preeminent worldwide cause of mortality. Early prediction and diagnosis HDs becomes very crucial save human kind. This study presents novel approach by integrating machine learning (ML) technique, explicitly, convolutional neural network (CNN) model with grasshopper optimization (GHO) algorithm optimize performance conventional CNN, thereby, efficiency accuracy proposed HD (HDP) enhanced. While evaluating on Cleveland Dataset, hybridized optimized CNN using GHO demonstrated superior metrics, namely, classification 88.52%, precision 87.87%, recall 90.62% F1-score 89.23%. The results emphasize model's potential robustness for early diagnosis, contributing significant improvements than ML methods. Further, strengthens growing body artificial intelligence (AI)-driven healthcare solutions highlights significance hybrid models in domain.

Язык: Английский

Процитировано

A Comprehensive Review on Heart Disease Risk Prediction using Machine Learning and Deep Learning Algorithms DOI

Karna Vishnu Vardhana Reddy, K. Viswavardhan Reddy, Varaprasad Janamala

и другие.

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Окт. 17, 2024

Язык: Английский

Процитировано

Exposomics and Cardiovascular Diseases: A Scoping Review of Machine Learning Approaches DOI

Katerina D. Argyri, Ioannis Gallos, Angelos Amditis

и другие.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июль 19, 2024

ABSTRACT Cardiovascular disease has been established as the world’s number one killer, causing over 20 million deaths per year. This fact, along with growing awareness of impact exposomic risk factors on cardiovascular diseases, led scientific community to leverage machine learning strategies a complementary approach traditional statistical epidemiological studies that are challenged by highly heterogeneous and dynamic nature exposomics data. The principal objective served this work is identify key pertinent literature provide an overview breadth research in field applications data focus diseases. Secondarily, we aimed at identifying common limitations meaningful directives be addressed future. Overall, shows that, despite fact under-researched compared its application other members -omics family, it increasingly adopted investigate different aspects

Язык: Английский

Процитировано