Intelligent Healthcare Platform: Cardiovascular Disease Risk Factors Prediction Using Attention Module Based LSTM

Islam, Samiul; Umran, Haider Muhamed; Umran, Haider M.; Karim, M. Rizal Syah

doi:10.1109/icaibd.2019.8836998

Cited by 18 publications

(4 citation statements)

References 26 publications

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“…Studies [ 142 , 143 ] showed that combining clinical variables and quantitative plaque metrics on CTA improved the prediction of ischemia using ML. Two DL architectures, including traditional CNN and LSTM, were used for obstructive case prediction [ 144 ] and cardiovascular disease risk factor prediction [ 145 ], respectively. Moreover, ensemble-based classification approaches, such as LogitBoost, decision tree, and XGBoost, have been adopted in predicting the risk for all-cause mortality [ 146 ], myocardial infarction cardiac death [ 147 ], calcification [ 148 ] and early revascularization [ 149 ].…”

Section: Resultsmentioning

confidence: 99%

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

Chen

et al. 2022

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Background: A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limited used in the cerebrovascular field even though the diseases can lead to catastrophic consequences. Objective: This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases. Methods: Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases. Results: For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. For the algorithms, both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion. Conclusion: Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.

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Section: Resultsmentioning

confidence: 99%

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

Chen

et al. 2022

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“…For example, Markov decision processes [42] and partially observable Markov decision processes [42] are leveraged for near real-time health monitoring, treatments, and interventions in various medical applications [43]. More recently, long-short term memory (LSTM), which is an artificial recurrent neural network (RNN) architecture effective for sequence modeling, has been applied to detect emotion [44], to predict cardiovascular disease risk factors [45], and to predict healthcare trajectories [46]. Machine learning is applied to smart homes [47][48][49].…”

Section: Sensor Data Analytics Via Machine Learning For Real-time Dec...mentioning

confidence: 99%

A Review of Efficient Real-Time Decision Making in the Internet of Things

Kang

2022

Technologies

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Emerging applications of IoT (the Internet of Things), such as smart transportation, health, and energy, are envisioned to greatly enhance the societal infrastructure and quality of life of individuals. In such innovative IoT applications, cost-efficient real-time decision-making is critical to facilitate, for example, effective transportation management and healthcare. In this paper, we formally define real-time decision tasks in IoT, review cutting-edge approaches that aim to efficiently schedule real-time decision tasks to meet their timing and data freshness constraints, review state-of-the-art approaches for efficient sensor data analytics in IoT, and discuss future research directions.

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“…With ToolDiag, RA obtained 50.00 percent accuracy using the IB1-4 algorithm. Utilizing InductH, WEKA, RA has a rating accuracy of 58.5 percent, whereas using the RBF, ToolDiag, RA has a rating accuracy of 60.00 percent [ 28 ]. ToolDiag employed the MLP + BP algorithm, which had a success rate of up to 65%.…”

Section: Literature Reviewmentioning

confidence: 99%

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

Shehzadi

Hassan

Rizwan

et al. 2022

Computational Intelligence and Neuroscience

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Ischemic heart disease (IHD) causes discomfort or irritation in the chest. According to the World Health Organization, coronary heart disease is the major cause of mortality in Pakistan. Accurate model with the highest precision is necessary to avoid fatalities. Previously several models are tried with different attributes to enhance the detection accuracy but failed to do so. In this research study, an artificial approach to categorize the current stage of heart disease is carried out. Our model predicts a precise diagnosis of chronic diseases. The system is trained using a training dataset and then tested using a test dataset. Machine learning methods such as LR, NB, and RF are applied to forecast the development of a disease. Experimental outcomes of this research study have proven that our strategy has excelled other procedures with maximum accuracy of 99 percent for RF, 97 percent for NB, and 98 percent for LR. With such high accuracy, the number of deaths per year of ischemic heart disease will be slightly decreased.

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Intelligent Healthcare Platform: Cardiovascular Disease Risk Factors Prediction Using Attention Module Based LSTM

Cited by 18 publications

References 26 publications

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

A Review of Efficient Real-Time Decision Making in the Internet of Things

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

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