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Gurumurthy P, Alagarsamy M, Kuppusamy S, Ponnusamy NC. M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images. NETWORK (BRISTOL, ENGLAND) 2024; 35:319-346. [PMID: 38279811 DOI: 10.1080/0954898x.2024.2306988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/11/2023] [Indexed: 01/29/2024]
Abstract
Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD. The M2AI-CVD framework employs a four-fold methodology: First, it rigorously evaluates image quality and processes lower-quality images for further analysis. Subsequently, it uses the Entropy-based Fuzzy C Means (EnFCM) algorithm for precise image segmentation. The Multi-Modal Boltzmann Machine (MMBM) is then employed to extract relevant features from various data modalities, while the Genetic Algorithm (GA) selects the most informative features. Finally, a ZFNet Convolutional Neural Network (ZFNetCNN) classifies images, effectively distinguishing between CVD and Non-CVD cases. The research's culmination, tested across five distinct datasets, yields outstanding results, with an accuracy of 95.89%, sensitivity of 96.89%, and specificity of 98.7%. This multi-modal AI approach offers a promising solution for the accurate and early detection of cardiovascular diseases, significantly improving the prospects of timely intervention and improved patient outcomes in the realm of cardiovascular health.
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Affiliation(s)
- Premalatha Gurumurthy
- Department of Electronics and Communication Engineering, Prathyusha Engineering College, Tiruvallur, Tamil Nadu, India
| | - Manjunathan Alagarsamy
- Department of Electronics and Communication Engineering, K.Ramakrishnan College of Technology, Trichy, Tamil Nadu, India
| | - Sangeetha Kuppusamy
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India
| | - Niranjana Chitra Ponnusamy
- Department of Electronics and Communication Engineering, Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India
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Kasim S, Amir Rudin PNF, Malek S, Ibrahim KS, Wan Ahmad WA, Fong AYY, Lin WY, Aziz F, Ibrahim N. Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI). Sci Rep 2024; 14:12378. [PMID: 38811643 PMCID: PMC11137033 DOI: 10.1038/s41598-024-61151-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/02/2024] [Indexed: 05/31/2024] Open
Abstract
The accurate prediction of in-hospital mortality in Asian women after ST-Elevation Myocardial Infarction (STEMI) remains a crucial issue in medical research. Existing models frequently neglect this demographic's particular attributes, resulting in poor treatment outcomes. This study aims to improve the prediction of in-hospital mortality in multi-ethnic Asian women with STEMI by employing both base and ensemble machine learning (ML) models. We centred on the development of demographic-specific models using data from the Malaysian National Cardiovascular Disease Database spanning 2006 to 2016. Through a careful iterative feature selection approach that included feature importance and sequential backward elimination, significant variables such as systolic blood pressure, Killip class, fasting blood glucose, beta-blockers, angiotensin-converting enzyme inhibitors (ACE), and oral hypoglycemic medications were identified. The findings of our study revealed that ML models with selected features outperformed the conventional Thrombolysis in Myocardial Infarction (TIMI) Risk score, with area under the curve (AUC) ranging from 0.60 to 0.93 versus TIMI's AUC of 0.81. Remarkably, our best-performing ensemble ML model was surpassed by the base ML model, support vector machine (SVM) Linear with SVM selected features (AUC: 0.93, CI: 0.89-0.98 versus AUC: 0.91, CI: 0.87-0.96). Furthermore, the women-specific model outperformed a non-gender-specific STEMI model (AUC: 0.92, CI: 0.87-0.97). Our findings demonstrate the value of women-specific ML models over standard approaches, emphasizing the importance of continued testing and validation to improve clinical care for women with STEMI.
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Affiliation(s)
- Sazzli Kasim
- Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
- Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh Campus, Sungai Buloh, Malaysia
| | | | - Sorayya Malek
- Institute of Biological Sciences, Faculty of Science, University Malaya, Kuala Lumpur, Malaysia.
| | - Khairul Shafiq Ibrahim
- Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
| | - Wan Azman Wan Ahmad
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
- Division of Cardiology, University Malaya Medical Centre (UMMC), Kuala Lumpur, Malaysia
| | - Alan Yean Yip Fong
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
- Department of Cardiology, Sarawak General Hospital, Kuching, Sarawak, Malaysia
| | - Wan Yin Lin
- Institute of Biological Sciences, Faculty of Science, University Malaya, Kuala Lumpur, Malaysia
| | - Firdaus Aziz
- School of Liberal Studies, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Nurulain Ibrahim
- Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh Campus, Sungai Buloh, Malaysia
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Hosseini Chagahi M, Mohammadi Dashtaki S, Moshiri B, Jalil Piran MD. Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation. Comput Biol Med 2024; 173:108345. [PMID: 38564852 DOI: 10.1016/j.compbiomed.2024.108345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/14/2024] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
Due to their widespread prevalence and impact on quality of life, cardiovascular diseases (CVD) pose a considerable global health burden. Early detection and intervention can reduce the incidence, severity, and progression of CVD and prevent premature death. The application of machine learning (ML) techniques to early CVD detection is therefore a valuable approach. In this paper, A stack-based ensemble classifier with an aggregation layer and the dependent ordered weighted averaging (DOWA) operator is proposed for detecting cardiovascular diseases. We propose transforming features using the Johnson transformation technique and normalizing feature distributions. Three diverse first-level classifiers are selected based on their accuracy, and predictions are combined using the aggregation layer and DOWA. A linear support vector machine (SVM) meta-classifier makes the final classification. Adding the aggregation layer to the stacking classifier improves classification accuracy significantly, according to the study. The accuracy is enhanced by 5%, resulting in an impressive overall accuracy of 94.05%. Moreover, the proposed system significantly increases the area under the receiver operating characteristic (ROC) curve compared to recent studies, reaching 97.14%. It further reinforces the classifier's reliability and effectiveness in classifying cardiovascular disease by distinguishing between positive and negative instances. With improved accuracy and a high area under the curve (AUC), the proposed classifier exhibits robustness and superior performance in the detection of cardiovascular diseases.
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Affiliation(s)
- Mehdi Hosseini Chagahi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Saeed Mohammadi Dashtaki
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Behzad Moshiri
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada.
| | - M D Jalil Piran
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, South Korea.
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Zhao M, Zhou H, Wang J, Liu Y, Zhang X. A new method for identification of traditional Chinese medicine constitution based on tongue features with machine learning. Technol Health Care 2024; 32:3393-3408. [PMID: 38875060 DOI: 10.3233/thc-240128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
BACKGROUND The theory of Chinese medicine (TCM) constitution contributes to the optimisation of individualised healthcare programmes. However, at present, TCM constitution identification mainly relies on inefficient questionnaires with subjective bias. Efficient and accurate TCM constitution identification can play an important role in individualised medicine and healthcare. OBJECTIVE Building an efficient model for identifying traditional Chinese medicine constitutions using objective tongue features and machine learning techniques. METHODS The DS01-A device was applied to collect tongue images and extract features. We trained and evaluated five machine learning models: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), LightGBM (LGBM), and CatBoost (CB). Among these, we selected the model with the best performance as the base classifier for constructing our heterogeneous ensemble learning model. Using various performance metrics, including classification accuracy, precision, recall, F1 score, and area under curve (AUC), to comprehensively evaluate model performance. RESULTS A total of 1149 tongue images were obtained and 45 features were extracted, forming dataset 1. RF, LGBM, and CB were selected as the base learners for the RLC-Stacking. On dataset 1, RLC-Stacking1 achieved an accuracy of 0.8122, outperforming individual classifiers. After feature selection, the classification accuracy of RLC-Stacking2 improved to 0.8287, an improvement of 0.00165 compared to RLC-Stacking1. RLC-Stacking2 achieved an accuracy exceeding 0.85 for identifying each TCM constitution type, indicating excellent identification performance. CONCLUSION The study provides a reliable method for the accurate and rapid identification of TCM constitutions and can assist clinicians in tailoring individualized medical treatments based on personal constitution types and guide daily health care. The information extracted from tongue images serves as an effective marker for objective TCM constitution identification.
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Arukonda S, Cheruku R. Nested genetic algorithm-based classifier selection and placement in multi-level ensemble framework for effective disease diagnosis. Comput Methods Biomech Biomed Engin 2023:1-24. [PMID: 38126276 DOI: 10.1080/10255842.2023.2294264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023]
Abstract
Effective disease diagnosis is a critical unmet need on a global scale. The intricacies of the numerous disease mechanisms and underlying symptoms make developing a model for early diagnosis and effective treatment extremely difficult. Machine learning (ML) can help to solve some of these issues. Recently, various ensemble-based ML models have benefited clinicians in early diagnosis. However, one of the most difficult challenges in multi-level ensemble approaches is the classifier selection and their placement in the ensemble framework as it improves the overall performance. Let m classifiers have to select from n classifiers there are ( n m ) ways. Again, these ( n m ) possibilities can be arranged in m ! ways. Finding the best m classifiers and their positions from total ( n m ) m ! ways is a challenging and hard problem. To address this challenge, a dynamic three-level ensemble framework is proposed. A nested Genetic Algorithm (GA) and ensemble-based fitness function are employed to optimize the classifier selection and their placement in a three-level ensemble framework. Our approach used eleven classifiers and chose seven classifiers by maximizing the fitness function. The proposed model experiments on 12 disease datasets. The proposed model outperformed in terms of accuracy, F1, and G-measure on the Chronic Kidney Disease (CKD) dataset is 0.987, 0.988, and 0.989, respectively. In terms of AUC on the Heart disease dataset (HDD) is 0.998 and in terms of recall on the Hypothyroid disease dataset (HyDD) is 0.988. In addition, the proposed model superiority is statically evaluated by Wilcoxon-Signed-Rank (WSR) test compared with other ensemble models, such as random forest (RF), bagging classifier (BC), XGBoost (XGB), and gradient boost classifier (GBC) with probability value p < 0.05 results shows all the traditional ensemble model differs with proposed model and also effective size evaluated with using the matched-pairs rank biserial correlation coefficient wc and statistical results shows effective size is large with RF and BC and effective size is medium with XGB and GBC. Proposed model has outperformed comparing with State-Of-The-Art (SOTA) ensemble and non-ensemble models. Further, the proposed model outperformed in terms of the ROC curve in the majority of the disease datasets. The results suggest the usage of the proposed model for disease diagnosis applications.
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Affiliation(s)
- Srinivas Arukonda
- Department of Computer Science and Engineering, National Institute of Technology Warangal, Hanamkonda, India
| | - Ramalingaswamy Cheruku
- Department of Computer Science and Engineering, National Institute of Technology Warangal, Hanamkonda, India
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Mahajan P, Uddin S, Hajati F, Moni MA. Ensemble Learning for Disease Prediction: A Review. Healthcare (Basel) 2023; 11:1808. [PMID: 37372925 DOI: 10.3390/healthcare11121808] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/19/2023] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016-2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses variability in the perceived performance of different ensemble approaches against frequently used disease datasets.
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Affiliation(s)
- Palak Mahajan
- College of Engineering and Science, Victoria University, Sydney, NSW 2000, Australia
| | - Shahadat Uddin
- School of Project Management, Faculty of Engineering, The University of Sydney, Forest Lodge, NSW 2037, Australia
| | - Farshid Hajati
- College of Engineering and Science, Victoria University, Sydney, NSW 2000, Australia
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia
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Hybrid Model Feature Selection with the Bee Swarm Optimization Method and Q-Learning on the Diagnosis of Coronary Heart Disease. INFORMATION 2022. [DOI: 10.3390/info14010015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Coronary heart disease is a type of cardiovascular disease characterized by atherosclerotic plaque, which causes myocardial infarction or sudden cardiac death. Since this sudden heart attack has no apparent symptoms, the early detection of the risk factors for coronary heart disease is required. Many studies have been conducted to diagnose heart disease, including studies that tested various classifiers, feature selection and detection models on several coronary heart disease datasets. As a result, this research aims to learn about the effect of the bee swarm optimization algorithm combined with Q-learning for optimizing the feature selection in improving the prediction of heart disease. This detection model was tested against various classification methods and evaluated against multiple performance measures, such as accuracy, precision, recall and the area under curve (AUC), to identify the best model for heart disease prediction and the benefit of the medical community. The test results show that the proposed method outperforms the existing process regarding the feature selection.
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