A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Algorithms, Journal Year: 2024, Volume and Issue: 17(2), P. 78 - 78

Published: Feb. 13, 2024

Cardiovascular disease is the leading cause of global mortality and responsible for millions deaths annually. The rate overall consequences cardiac can be reduced with early detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment misdiagnoses, which impede course raise healthcare costs. application artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes central role in health focuses on precise cardiovascular prediction. In particular, this driven by urgent need fully utilize potential enhance light continued progress growing public implications disease, aims offer comprehensive analysis topic. review encompasses wide range topics, types significance learning, feature selection, evaluation models, data collection & preprocessing, metrics prediction, recent trends suggestion future works. addition, holistic view learning’s prediction health. We believe that our will contribute significantly existing body knowledge essential area.

Language: Английский

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Multilayer Perceptron Neural Network with Arithmetic Optimization Algorithm-Based Feature Selection for Cardiovascular Disease Prediction

Machine Learning and Knowledge Extraction, Journal Year: 2024, Volume and Issue: 6(2), P. 987 - 1008

Published: May 5, 2024

In the healthcare field, diagnosing disease is most concerning issue. Various diseases including cardiovascular (CVDs) significantly influence illness or death. On other hand, early and precise diagnosis of CVDs can decrease chances death, resulting in a better healthier life for patients. Researchers have used traditional machine learning (ML) techniques CVD prediction classification. However, many them are inaccurate time-consuming due to unavailability quality data imbalanced samples, inefficient preprocessing, existing selection criteria. These factors lead an overfitting bias issue towards certain class label model. Therefore, intelligent system needed which accurately diagnose CVDs. We proposed automated ML model various kinds Our consists multiple steps. Firstly, benchmark dataset preprocessed using filter techniques. Secondly, novel arithmetic optimization algorithm implemented as feature technique select best subset features that accuracy Thirdly, classification task multilayer perceptron neural network classify instances into two labels, determining whether they not. The trained on then tested validated. Furthermore, comparative analysis model, performance evaluation metrics calculated overall accuracy, precision, recall, F1-score. As result, it has been observed achieve 88.89% highest comparison with

Language: Английский

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Heart Disease Prediction Using Novel Quine McCluskey Binary Classifier (QMBC)

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 64324 - 64347

Published: Jan. 1, 2023

Cardiovascular disease is the primary reason for mortality worldwide, responsible around a third of all deaths. To assist medical professionals in quickly identifying and diagnosing patients, numerous machine learning data mining techniques are utilized to predict disease. Many researchers have developed various models boost efficiency these predictions. Feature selection extraction remove unnecessary features from dataset, thereby reducing computation time increasing models. In this study, we introduce new ensemble Quine McCluskey Binary Classifier (QMBC) technique patients diagnosed with some form heart those who not diagnosed. The QMBC model utilizes an seven models, including logistic regression, decision tree, random forest, K-nearest neighbour, naive bayes, support vector machine, multilayer perceptron, performs exceptionally well on binary class datasets. We employ feature accelerate prediction process. utilize Chi-Square ANOVA approaches identify top 10 create subset dataset. then apply Principal Component Analysis 9 prime components. obtain Minimum Boolean expression target feature. results ( x ₀ , xmlns:xlink="http://www.w3.org/1999/xlink">1 xmlns:xlink="http://www.w3.org/1999/xlink">2 ..., xmlns:xlink="http://www.w3.org/1999/xlink">6 ) considered independent features, while attribute dependent. combine projected outcomes ML foaming utilizing minimum equation built 80:20 train-to-test ratio. Our proposed surpasses current state-of-the-art previously suggested methods put forward by researchers.

Language: Английский

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Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e1917 - e1917

Published: March 18, 2024

Heart disease is one of the primary causes morbidity and death worldwide. Millions people have had heart attacks every year, only early-stage predictions can help to reduce number. Researchers are working on designing developing prediction systems using different advanced technologies, machine learning (ML) them. Almost all existing ML-based works consider same dataset (intra-dataset) for training validation their method. In particular, they do not inter-dataset performance checks, where datasets used in testing phases. setup, ML models show a poor named discrepancy problem. This work focuses mitigating problem by considering five available combined form. All potential mode combinations systematically executed assess discrepancies before after applying proposed methods. Imbalance data handling SMOTE-Tomek, feature selection random forest (RF), extraction principle component analysis (PCA) with long preprocessing pipeline mitigate The builds missing value RF regression, log transformation, outlier removal, normalization, balancing that convert more ML-centric. Support vector machine, K-nearest neighbors, decision tree, RF, eXtreme Gradient Boosting, Gaussian naive Bayes, logistic multilayer perceptron as classifiers. Experimental results classification produce better than other combination strategies both single- setups. certain configurations individual datasets, demonstrates 100% accuracy 96% during phase an exhibiting commendable precision, recall, F1 score, specificity, AUC score. indicate effective technique has improve model without necessitating development intricate models. Addressing introduces novel research avenue, enabling amalgamation identical features from various construct comprehensive global within specific domain.

Language: Английский

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Injury Patterns and Impact on Performance in the NBA League Using Sports Analytics

Computation, Journal Year: 2024, Volume and Issue: 12(2), P. 36 - 36

Published: Feb. 16, 2024

This research paper examines Sports Analytics, focusing on injury patterns in the National Basketball Association (NBA) and their impact players’ performance. It employs a unique dataset to identify common NBA injuries, determine most affected anatomical areas, analyze how these injuries influence post-recovery study’s novelty lies its integrative approach that combines data with performance metrics salary data, providing new insights into relationship between economic on-court investigates periodicity seasonality of seeking related time external factors. Additionally, it effect specific per-match analytics performance, offering perspectives implications rehabilitation for player contributes significantly sports analytics, assisting coaches, medicine professionals, team management developing prevention strategies, optimizing rotations, creating targeted plans. Its findings illuminate interplay salaries, NBA, aiming enhance welfare league’s overall competitiveness. With comprehensive sophisticated analysis, this offers unprecedented dynamics long-term effects athletes.

Language: Английский

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TPTM-HANN-GA: A Novel Hyperparameter Optimization Framework Integrating the Taguchi Method, an Artificial Neural Network, and a Genetic Algorithm for the Precise Prediction of Cardiovascular Disease Risk

Mathematics, Journal Year: 2024, Volume and Issue: 12(9), P. 1303 - 1303

Published: April 25, 2024

The timely and precise prediction of cardiovascular disease (CVD) risk is essential for effective prevention intervention. This study proposes a novel framework that integrates the two-phase Taguchi method (TPTM), hyperparameter artificial neural network (HANN), genetic algorithm (GA) called TPTM-HANN-GA. efficiently optimizes hyperparameters an (ANN) model during training stage, significantly enhancing accuracy risk. proposed TPTM-HANN-GA requires far fewer experiments than traditional grid search, making it highly suitable application in resource-constrained, low-power computers, edge intelligence (edge AI) devices. Furthermore, successfully identified optimal configurations ANN model’s hyperparameters, resulting hidden layer 4 nodes, tanh activation function, SGD optimizer, learning rate 0.23425849, momentum 0.75462782, seven nodes. optimized achieves 74.25% predicting disease, which exceeds existing state-of-the-art GA-ANN TSTO-ANN models. enables personalized CVD to be conducted on computers edge-AI devices, achieving goal point-of-care testing (POCT) empowering individuals manage their heart health effectively.

Language: Английский

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An offbeat bolstered swarm integrated ensemble learning (BSEL) model for heart disease diagnosis and classification

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 154, P. 111273 - 111273

Published: Jan. 23, 2024

Language: Английский

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A Comprehensive Review of Machine Learning Algorithms and Its Application in Groundwater Quality Prediction

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: June 24, 2024

Language: Английский

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The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease

Biomedicines, Journal Year: 2025, Volume and Issue: 13(2), P. 427 - 427

Published: Feb. 10, 2025

The application of artificial intelligence (AI) and machine learning (ML) in medicine healthcare has been extensively explored across various areas. AI ML can revolutionize cardiovascular disease management by significantly enhancing diagnostic accuracy, prediction, workflow optimization, resource utilization. This review summarizes current advancements concerning disease, including their clinical investigation use primary cardiac imaging techniques, common categories, research, patient care, outcome prediction. We analyze discuss commonly used models, algorithms, methodologies, highlighting roles improving outcomes while addressing limitations future applications. Furthermore, this emphasizes the transformative potential practice decision making, reducing human error, monitoring support, creating more efficient workflows for complex conditions.

Language: Английский

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Predicting incident cardio-metabolic disease among persons with and without depressive and anxiety disorders: a machine learning approach

Social Psychiatry and Psychiatric Epidemiology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

Language: Английский

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