Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models

European journal of medical research, Journal Year: 2024, Volume and Issue: 29(1)

Published: Sept. 11, 2024

The incidence and mortality rates of cardiovascular disease worldwide are a major concern in the healthcare industry. Precise prediction is essential, use machine learning deep can aid decision-making enhance predictive abilities.

Language: Английский

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Advanced Hybrid Machine Learning Model for Accurate Detection of Cardiovascular Disease

International Journal of Computational Intelligence Systems, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 6, 2025

Cardiovascular disease (CVD) is one of the foremost reasons behind death people worldwide. Prevention and early diagnosis are only ways to control its progression onset. Thus, there an urgent need for a detection model comprising intelligent technologies, including Machine Learning (ML) deep learning, predict future state individual suffering from cardiovascular by effectively analyzing patient data. This study aims propose hybrid that provides insight into data under consideration enhance accuracy detecting disease. current research proposes four stages. In first stage proposed model, imbalance problem solved using sampling technique named Synthetic Minority Oversampling Technique-Edited Nearest Neighbors Rule. second stage, Chi-square applied as feature selection method select highly relevant features records 1190 with 11 clinical features, curated combining 5 most popular datasets, Long Beach VA, Hungarian, Switzerland, Statlog (Heart). third preprocessed dataset passed stacking ensemble three base learners: Random Forest Tree (RFT), K-Nearest Neighbor (K-NN), AdaBoost classifier meta-learner: Logistic Regression (LR), optimized Grid Search Cross-Validation (GSCV) optimization approach, whose performance evaluated against classifier. fourth in terms accuracy, sensitivity, specificity, F1 score, ROC_AUC score.. The comparative results prove scored highest 97.8%, 96.15% 96.75% specificity 98.6% score when compared existing techniques models after applying SMOTE–ENN (for balancing) selection) methods efficient implementation demonstrate suggested may accurately identify among patients. It facilitates application robust treatment strategies.

Language: Английский

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FFS-IML: fusion-based statistical feature selection for machine learning-driven interpretability of chronic kidney disease

International Journal of Machine Learning and Cybernetics, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

Language: Английский

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Enhanced heart disease risk prediction using adaptive botox optimization based deep long-term recurrent convolutional network

Technology and Health Care, Journal Year: 2025, Volume and Issue: unknown

Published: April 30, 2025

Background Heart disease is the leading cause of death worldwide and predicting it a complex task requiring extensive expertise. Recent advancements in IoT-based illness prediction have enabled accurate classification using sensor data. Objective This research introduces methodology for heart classification, integrating advanced data preprocessing, feature selection, deep learning (DL) techniques tailored IoT Methods The work employs Clustering-based Data Imputation Normalization (CDIN) Robust Mahalanobis Distance-based Outlier Detection (RMDBOD) ensuring quality. Feature selection achieved Improved Binary Quantum-based Avian Navigation Optimization (IBQANO) algorithm, performed with Deep Long-Term Recurrent Convolutional Network (DLRCN), fine-tuned Adaptive Botox Algorithm (ABOA). Results proposed models tested on Hungarian, UCI, Cleveland datasets demonstrate significant improvements over existing methods. Specifically, dataset model achieves an accuracy 99.72%, while UCI 99.41%. Conclusion represents advancement remote healthcare monitoring, crucial managing conditions like high blood pressure, especially older adults, offering reliable solution prediction.

Language: Английский

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Enhanced Feature Selection Using Quantum-Inspired Cuckoo Search and Machine Learning for Heart Disease Prediction

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 339 - 372

Published: May 2, 2025

Heart disease remains a leading global health challenge demanding accurate predictive models for early diagnosis. Traditional machine learning (ML) struggle with high-dimensional data, feature selection, and interpretability in clinical settings. To address these challenges, we propose Quantum-Inspired Cuckoo Search Feature Selection Algorithm (QICSFA) integrating quantum principles optimized selection. Experimental results show that QICSFA combined Bayesian Optimization (BO) achieves 97% accuracy XGB 96% RF by outclassing conventional methods. The key features such as maximum heart rate (Thalach), chest pain type (Cp), ST depression (Oldpeak) align known cardiovascular risk factors to ensure relevance. In the future, this study establishes scalable AI-driven diagnostic tool potential applications real-time patient monitoring, multi-institutional dataset validation, explainable AI (XAI) integration, enhancing trust adoption healthcare systems.

Language: Английский

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An Integrated Stacked Convolutional Neural Network and the Levy Flight-based Grasshopper Optimization Algorithm for Predicting Heart Disease

Healthcare Analytics, Journal Year: 2024, Volume and Issue: unknown, P. 100374 - 100374

Published: Dec. 1, 2024

Language: Английский

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Integrating Ant Colony Optimization With Deep Learning for Improved Kidney Disease Diagnosis and Prognosis

Advances in computer and electrical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 175 - 192

Published: Dec. 6, 2024

Accurate and early diagnosis of kidney cancer is critical for effective treatment improved patient outcomes, yet current methods often face challenges in precision reliability. This research addresses these by integrating Ant Colony Optimization (ACO) with advanced deep learning models—DenseNet, ResNet 50, VGG 19—and Long Short-Term Memory (LSTM) networks to enhance the prediction classification from CT scans medical records. The approach leverages ACO optimise feature selection, improving performance models. DenseNet, combined LSTM, achieved highest accuracy 97.9%, demonstrating exceptional capability accurately detecting classifying cancer. Res-Net also optimised ACO, followed a notable 96.2%, showing its robustness. 19, despite substantial improvement over training epochs, attained lower 92.3%, indicating that further optimisation could be beneficial.

Language: Английский

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A Hybrid Transfer Learning Approach Using Obesity Data for Predicting Cardiovascular Diseases Incorporating Lifestyle Factors

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

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