Forecasting Heart Disease Risk with a Stacking-Based Ensemble Machine Learning Method DOI Open Access

Yuanyuan Wu,

Zhuomin Xia,

Zikai Feng

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(20), P. 3996 - 3996

Published: Oct. 11, 2024

As one of the main causes sickness and mortality, heart disease, also known as cardiovascular must be detected early in order to prevented treated. The rapid development computer technology presents an opportunity for cross-combination medicine informatics. A novel stacking model called SDKABL is presented this work. It uses three classifiers, namely K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM) at base layer Bidirectional Long Short-Term Memory based on Attention Mechanisms (ABiLSTM) meta ultimate prediction. For lowering temporal complexity enhancing model’s accuracy, dimensionality reduction approach seen crucial. Principal Component Analysis (PCA) was utilized minimize facilitate feature fusion. Using several performance measures, including precision, F1-score, recall, Receiver Operating Characteristic (ROC) score, compared that other independent classifiers. experimental findings demonstrate our proposed combining individual classifiers with method helps improve prediction accuracy.

Language: Английский

Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders DOI Creative Commons
Andrea Calderone, Dèsiréè Latella, Mirjam Bonanno

et al.

Biomedicines, Journal Year: 2024, Volume and Issue: 12(10), P. 2415 - 2415

Published: Oct. 21, 2024

Background and Objectives: Neurological disorders like stroke, spinal cord injury (SCI), Parkinson’s disease (PD) significantly affect global health, requiring accurate diagnosis long-term neurorehabilitation. Artificial intelligence (AI), such as machine learning (ML), may enhance early diagnosis, personalize treatment, optimize rehabilitation through predictive analytics, robotic systems, brain-computer interfaces, improving outcomes for patients. This systematic review examines how AI ML systems influence treatment in neurorehabilitation among neurological disorders. Materials Methods: Studies were identified from an online search of PubMed, Web Science, Scopus databases with a time range 2014 to 2024. has been registered on Open OSF (n) EH9PT. Results: Recent advancements are revolutionizing motor conditions SCI, PD, offering new opportunities personalized care improved outcomes. These technologies clinical assessments, therapy personalization, remote monitoring, providing more precise interventions better management. Conclusions: is neurorehabilitation, personalized, data-driven treatments that recovery Future efforts should focus large-scale validation, ethical considerations, expanding access advanced, home-based care.

Language: Английский

Citations

20
PD_EBM: An Integrated Boosting Approach Based on Selective Features for Unveiling Parkinson's Disease Diagnosis With Global and Local Explanations DOI Creative Commons
Fahmida Khanom, Mohammad Shorif Uddin, Rafid Mostafiz

et al.

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 1, 2025

ABSTRACT Early detection and characterization are crucial for treating managing Parkinson's disease (PD). The increasing prevalence of PD its significant impact on the motor neurons brain impose a substantial burden healthcare system. Early‐stage is vital improving patient outcomes reducing costs. This study introduces an ensemble boosting machine, termed PD_EBM, PD. PD_EBM leverages machine learning (ML) algorithms hybrid feature selection approach to enhance diagnostic accuracy. While ML has shown promise in medical applications detection, interpretability these models remains challenge. Explainable (XML) addresses this by providing transparency clarity model predictions. Techniques such as Local Interpretable Model‐agnostic Explanations (LIME) SHapley Additive exPlanations (SHAP) have become popular interpreting models. Our experiment used dataset 195 clinical records patients from University California Irvine (UCI) Machine Learning repository. Comprehensive data preparation included encoding categorical features, imputing missing values, removing outliers, addressing imbalance, scaling data, selecting relevant so on. We propose framework that focuses most important features prediction. employs Decision Tree (DT) classifier with AdaBoost, followed linear discriminant analysis (LDA) optimizer, achieving impressive accuracy 99.44%, outperforming other

Language: Английский

Citations

1
Artificial intelligence-enabled detection and assessment of Parkinson’s disease using multimodal data: A survey DOI
Aite Zhao, Yongcan Liu,

Xinglin Yu

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103175 - 103175

Published: April 1, 2025

Language: Английский

Citations

0
A multi-layered stacked classifier developed for diagnosis of Parkinson’s disease and SWEDD patients using fusion of multimodal data DOI

Nikita Aggarwal,

Barjinder Singh Saini, Savita Gupta

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 108, P. 107924 - 107924

Published: April 23, 2025

Language: Английский

Citations

0
XEMLPD: an explainable ensemble machine learning approach for Parkinson disease diagnosis with optimized features DOI
Fahmida Khanom, S. K. Biswas, Mohammad Shorif Uddin

et al.

International Journal of Speech Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 16, 2024

Language: Английский

Citations

3
A Hybrid Framework of Transformer Encoder and Residential Conventional for Cardiovascular Disease Recognition Using Heart Sounds DOI Creative Commons
Riyadh M. Al-Tam, Aymen M. Al-Hejri, Ebrahim Naji

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 123099 - 123113

Published: Jan. 1, 2024

Language: Английский

Citations

1
Forecasting Heart Disease Risk with a Stacking-Based Ensemble Machine Learning Method DOI Open Access

Yuanyuan Wu,

Zhuomin Xia,

Zikai Feng

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(20), P. 3996 - 3996

Published: Oct. 11, 2024

As one of the main causes sickness and mortality, heart disease, also known as cardiovascular must be detected early in order to prevented treated. The rapid development computer technology presents an opportunity for cross-combination medicine informatics. A novel stacking model called SDKABL is presented this work. It uses three classifiers, namely K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM) at base layer Bidirectional Long Short-Term Memory based on Attention Mechanisms (ABiLSTM) meta ultimate prediction. For lowering temporal complexity enhancing model’s accuracy, dimensionality reduction approach seen crucial. Principal Component Analysis (PCA) was utilized minimize facilitate feature fusion. Using several performance measures, including precision, F1-score, recall, Receiver Operating Characteristic (ROC) score, compared that other independent classifiers. experimental findings demonstrate our proposed combining individual classifiers with method helps improve prediction accuracy.

Language: Английский

Citations

0