Revolutionizing cardiovascular health: integrating deep learning techniques for predictive analysis of personal key indicators in heart disease DOI Creative Commons
Fatma M. Talaat

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 15, 2024

Abstract Cardiovascular diseases (CVDs) remain a global burden, highlighting the need for innovative approaches early detection and intervention. This study investigates potential of deep learning, specifically convolutional neural networks (CNNs), to improve prediction heart disease risk using key personal health markers. Our approach revolutionizes traditional healthcare predictive modeling by integrating CNNs, which excel at uncovering subtle patterns hidden interactions among various indicators such as blood pressure, cholesterol levels, lifestyle factors. To achieve this, we leverage advanced network architectures. The model utilizes embedding layers transform categorical data into numerical representations, extract spatial features, dense complex predict CVD risk. Regularization techniques like dropout batch normalization, along with hyperparameter optimization, enhance generalizability performance. Rigorous validation against conventional methods demonstrates model’s superiority, significantly higher R 2 value 0.994. achievement underscores valuable tool clinicians in prevention management. also emphasizes interpretability learning models addresses ethical considerations ensure responsible implementation clinical practice.

Language: Английский

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

Shamsuddin Sultan,

Nadeem Javaid, Nabil Alrajeh

et al.

Symmetry, Journal Year: 2025, Volume and Issue: 17(2), P. 185 - 185

Published: Jan. 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.

Language: Английский

Citations

0
Enhancing Cardiovascular Disease Prediction Accuracy Through Optimized Machine Learning and Deep Learning Techniques DOI

S. R. Divyasri,

P S Sreeja

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 379 - 391

Published: Jan. 1, 2025

Language: Английский

Citations

0
Extra Trees Model for Heart Disease Prediction DOI Open Access
Uchenna J. Nzenwata,

Emil Edwin,

Ebere O. Chukwu

et al.

Journal of Data Analysis and Information Processing, Journal Year: 2025, Volume and Issue: 13(02), P. 125 - 139

Published: Jan. 1, 2025

Language: Английский

Citations

0
Toward precision cardiology: a transformer-based system for adaptive prediction of heart disease DOI
Fatma M. Talaat, W Aly

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

Language: Английский

Citations

0
Predictive analysis of heart disease using quantum-assisted machine learning DOI Creative Commons
Mehroush Banday,

Sherin Zafar,

Parul Agarwal

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 7(5)

Published: May 3, 2025

Language: Английский

Citations

0
CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease DOI Creative Commons
Fatma M. Talaat,

Ahmed R. Elnaggar,

Warda M. Shaban

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(8), P. 822 - 822

Published: Aug. 12, 2024

The global prevalence of cardiovascular diseases (CVDs) as a leading cause death highlights the imperative need for refined risk assessment and prognostication methods. traditional approaches, including Framingham Risk Score, blood tests, imaging techniques, clinical assessments, although widely utilized, are hindered by limitations such lack precision, reliance on static variables, inability to adapt new patient data, thereby necessitating exploration alternative strategies. In response, this study introduces CardioRiskNet, hybrid AI-based model designed transcend these limitations. proposed CardioRiskNet consists seven parts: data preprocessing, feature selection encoding, eXplainable AI (XAI) integration, active learning, attention mechanisms, prediction prognosis, evaluation validation, deployment integration. At first, preprocessed cleaning handling missing values, applying normalization process, extracting features. Next, most informative features selected categorical variables converted into numerical form. Distinctively, employs learning iteratively select samples, enhancing its efficacy, while mechanism dynamically focuses relevant precise prediction. Additionally, integration XAI facilitates interpretability transparency in decision-making processes. According experimental results, demonstrates superior performance terms accuracy, sensitivity, specificity, F1-Score, with values 98.7%, 99%, respectively. These findings show that can accurately assess prognosticate CVD risk, demonstrating power surpass conventional Thus, CardioRiskNet's novel approach high advance management CVDs provide healthcare professionals powerful tool care.

Language: Английский

Citations

3
Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk DOI Creative Commons
Chia-Ming Lin, Yu‐Shiang Lin

Bioengineering, Journal Year: 2023, Volume and Issue: 10(11), P. 1286 - 1286

Published: Nov. 4, 2023

The complexity of cardiovascular disease onset emphasizes the vital role early detection in prevention. This study aims to enhance prediction accuracy using personal devices, aligning with point-of-care testing (POCT) objectives. introduces a two-stage Taguchi optimization (TSTO) method boost predictive an artificial neural network (ANN) model while minimizing computational costs. In first stage, optimal hyperparameter levels and trends were identified. second stage determined best settings for ANN model's hyperparameters. this study, we applied proposed TSTO computer Kaggle Cardiovascular Disease dataset. Subsequently, identified setting hyperparameters model, hidden layer 4, activation function tanh, optimizer SGD, learning rate 0.25, momentum 0.85, nodes 10. led state-of-the-art 74.14% predicting risk disease. Moreover, significantly reduced number experiments by factor 40.5 compared traditional grid search method. accurately predicts conserves resources. It is adaptable low-power aiding goal POCT.

Language: Английский

Citations

7
A comprehensive review on heart disease prognostication using different artificial intelligence algorithms DOI
A. Jainul Fathima, M. M. Noor

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2024, Volume and Issue: 27(11), P. 1357 - 1374

Published: Feb. 29, 2024

Prediction of heart diseases on time is significant in order to preserve life. Many conventional methods have taken efforts earlier prediction but faced with challenges higher cost, extended for computation and complexities larger volume data which reduced accuracy. In overcome such pitfalls, AI (Artificial Intelligence) technology has been evolved diagnosing through deployment several ML (Machine Learning) DL (Deep algorithms. It improves detection by influencing its capacity learning from the massive containing age, obesity, hypertension other risk factors patients extract it accordingly differentiate circumstances. Moreover, storage greatly assists analysing occurrence disease past historical data. Hence, this paper intends provide a review different based algorithms used prognostication delivers benefits researching various existing works. performs comparative analysis critical assessment as encompassing accuracies maximum utilization focussed traditional studies area. The major findings emphasized evolution continuous explorations techniques future researchers aims determining dimensions that attained high low appropriate research works can be performed. Finally, included offer new stimulus further investigation cardiac diagnosis.

Language: Английский

Citations

2
Explainable AI assisted heart disease diagnosis through effective feature engineering and stacked ensemble learning DOI
Partho Ghose, Khondokar Oliullah, Md. Kawsher Mahbub

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125928 - 125928

Published: Nov. 1, 2024

Language: Английский

Citations

2
The application of artificial intelligence in diabetic retinopathy screening: a Saudi Arabian perspective DOI Creative Commons

Abdulaziz A. Barakat,

Omar Mobarak,

Haroon Ahmed Javaid

et al.

Frontiers in Medicine, Journal Year: 2023, Volume and Issue: 10

Published: Nov. 22, 2023

Introduction Diabetic retinopathy (DR) is the leading cause of preventable blindness in Saudi Arabia. With a prevalence up to 40% patients with diabetes, DR constitutes significant public health burden on country. Arabia has not yet established national screening program for DR. Mounting evidence shows that Artificial intelligence (AI)-based programs are slowly becoming superior traditional screening, COVID-19 pandemic accelerating research into this topic as well changing outlook toward it. The main objective study evaluate perception and acceptance AI among eye care professionals Methods A cross-sectional using self-administered online-based questionnaire was distributed by email through registry Commission For Health Specialties (SCFHS). 309 ophthalmologists physicians involved diabetic participated study. Data analysis done SPSS, value p < 0.05 considered statistical purposes. Results 54% participants rated their level knowledge above average 63% believed telemedicine interchangeable. 66% would decrease workforce physicians. 79% expected clinical efficiency increase AI. Around 50% be implemented next 5 years. Discussion Most reported good about Physicians more experience those who used e-health apps practice regarded higher than peers. Perceived strongly related benefits AI-based screening. In general, there positive attitude However, concerns labor market data confidentiality were evident. There should further education awareness topic.

Language: Английский

Citations

5