Predicting Stroke from Electronic Health Records

Nwosu, Chidozie Shamrock; Dev, Soumyabrata; Bhardwaj, Peru; Veeravalli, Bharadwaj; John, Deepu

doi:10.1109/embc.2019.8857234

Cited by 62 publications

(29 citation statements)

References 12 publications

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“…Research paper [7] shows that the model was trained using Decision Tree, Random Forest, and Multi-layer perceptron for stroke prediction. The obtained accuracies for the three methods were quite close, with slight differences.…”

Section: Literature Surveymentioning

confidence: 99%

Analyzing the Performance of Stroke Prediction using ML Classification Algorithms

Sailasya¹,

Kumari²

2021

IJACSA

124

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Stroke is a health condition that causes damage by tearing the blood vessels in the brain. It can also occur when there is a halt in the blood flow and other nutrients to the brain. According to the World Health Organization (WHO), stroke is the leading cause of death and disability globally. Most of the work has been carried out on the prediction of heart stroke but very few works show the risk of a brain stroke. With this thought, various machine learning models are built to predict the possibility of stroke in the brain. This paper has taken various physiological factors and used machine learning algorithms like Logistic Regression, Decision Tree Classification, Random Forest Classification, K-Nearest Neighbors, Support Vector Machine and Naïve Bayes Classification to train five different models for accurate prediction. The algorithm that best performed this task is Naïve Bayes that gave an accuracy of approximately 82%.

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Section: Literature Surveymentioning

confidence: 99%

Analyzing the Performance of Stroke Prediction using ML Classification Algorithms

Sailasya¹,

Kumari²

2021

IJACSA

124

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“…A quick literature review found a few studies using various machine-learning techniques, including artificial neural networks (ANN), for stroke diagnosis or prediction [38][39][40][41][42]. For example, Shanthi et al [38] reported that an individual's risk rate for stroke can be detected using ANN based on stroke patient data.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, they used the backpropagation algorithm for learning, and showed improvements in consistency and diagnostic accuracy for the prediction. Nwosu et al [39] studied the analysis and prediction of risk factors associated with the onset of stroke using data mining techniques and individual patient electronic health records. According to the experimental results, the prediction accuracy of decision tree (DT) was 74.31%, Random Forest was 74.53%, and ANN algorithm was 75.02%.…”

Section: Related Workmentioning

confidence: 99%

Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals

et al. 2021

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Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (α), beta (β), gamma (γ), delta (δ), and theta (θ) as well as the low β, high β, and θ to β ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future.

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“…This lead to increased vehicular emissions and related green-house gas emissions to the world. The degradation of the air quality caused serious long-term damage to the lungs, heart disease [1], and other respiratory diseases [2]. Therefore, remote sensing analysts continually recorded the amount of atmospheric pollutants.…”

Section: Introductionmentioning

confidence: 99%

Impact of Covid19-Induced Lockdown on Air Quality in Ireland

Kaloni

Lee

Dev

2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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Air pollution has been a long-existing problem for most of the major metropolitan cities of the world. Several measures including strict climate laws and reduction in the number of vehicles were implemented by several nations. However, in the recent wake of the COVID19 pandemic, there has been a renewed interest in revisiting the problem of low air quality. Several countries implemented strict lockdown measures halting the vehicular traffic and other economic activities, in order to reduce the spread of COVID19. In this paper, we analyze the impact of such COVID19-induced lockdown on the air quality of the atmosphere. Our case study is based in the city of Dublin, Ireland. We analyze the average concentration of common gaseous pollutant majorly responsible for industrial and vehicular pollution, viz. nitrogen dioxide (NO 2 ). These concentrations are obtained from the tropospheric column of the atmosphere collected by Sentinel-5P, which is an earth observation satellite of European Space Agency. We observe that Dublin had a significant drop in the level of NO 2 concentration, owing to the strict lockdown measures implemented across the nation.

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Predicting Stroke from Electronic Health Records

Cited by 62 publications

References 12 publications

Analyzing the Performance of Stroke Prediction using ML Classification Algorithms

Analyzing the Performance of Stroke Prediction using ML Classification Algorithms

Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals

Impact of Covid19-Induced Lockdown on Air Quality in Ireland

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