The Use of Deep Learning to Predict Stroke Patient Mortality

Cheon, Song-Hee; Kim, Jungyoon; Lim, Jihye

doi:10.3390/ijerph16111876

Cited by 108 publications

(66 citation statements)

References 51 publications

(49 reference statements)

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“…The multiple end-to-end network models proposed in this paper realized the feature fusion of multi-modal data and stroke prediction. We compared the method proposed in this paper with the current stroke prediction methods [ 10 , 11 ], as shown in Table 8 . First, the method proposed in this paper has made perfect measures in terms of input data, changing from universal single-modal data to multi-modal data.…”

Section: Discussionmentioning

confidence: 99%

“…In terms of the prediction of stroke, many researchers used artificial intelligence technology to predict stroke. For example, Songhee [ 10 ] used a deep neural network based on extended PCA to extract features from medical service usage and health behavior data and predicted stroke; the area under the curve (AUC) value of our method was 83.48%. It can be used by both patients and doctors to prescreen for possible strokes; however, the risk factors considered are not comprehensive, and the predictive performance of the model is average.…”

Section: Introductionmentioning

confidence: 99%

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The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model

Liu

Yin

Yang

2020

Sensors

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As is known, cerebral stroke has become one of the main diseases endangering people’s health; ischaemic strokes accounts for approximately 85% of cerebral strokes. According to research, early prediction and prevention can effectively reduce the incidence rate of the disease. However, it is difficult to predict the ischaemic stroke because the data related to the disease are multi-modal. To achieve high accuracy of prediction and combine the stroke risk predictors obtained by previous researchers, a method for predicting the probability of stroke occurrence based on a multi-model fusion convolutional neural network structure is proposed. In such a way, the accuracy of ischaemic stroke prediction is improved by processing multi-modal data through multiple end-to-end neural networks. In this method, the feature extraction of structured data (age, gender, history of hypertension, etc.) and streaming data (heart rate, blood pressure, etc.) based on a convolutional neural network is first realized. A neural network model for feature fusion is then constructed to realize the feature fusion of structured data and streaming data. Finally, a predictive model for predicting the probability of stroke is obtained by training. As shown in the experimental results, the accuracy of ischaemic stroke prediction reached 98.53%. Such a high prediction accuracy will be helpful for preventing the occurrence of stroke.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model

Liu

Yin

Yang

2020

Sensors

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“…e study demonstrates that privacy of pictures uploaded by a user on social media is important, and hence, it is important that machine learning models can automatically predict whether the privacy of pictures uploaded on social media should be public or private. Deep learning algorithms along with PCA have been in making prediction of Stroke Patient Mortality in [28]. e paper demonstrates that the area under the curve of the proposed method based on deep learning was 83.48% and therefore can be effectively used by patients and doctors to prescreen for possible stroke.…”

Section: Related Workmentioning

confidence: 96%

An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

et al. 2020

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Organizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, organizations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or will be habitually absent. For some organizations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the organization. In this paper, we propose Neural Networks and Deep Learning algorithms that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. The proposed model will provide a useful mechanism to organizations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. This paper is a first study of its kind to analyze the patterns of absenteeism in employees using deep learning algorithms and helps the organization to further improve the quality of life of employees and hence reduce absenteeism.

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“…This results in a substantial economic burden to Korea, with the total nationwide cost for stroke care nearly 3.3 billion US dollars in 2005 [1]. The duration of hospital stay and medical expenditure is higher in stroke patients than patients with other chronic diseases [8]. Therefore, an early diagnosis of stroke can enable us to save the lives of people, and the research on stroke patients is also very important for the effective utilization of medical resources.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of ECG Features for the Classification of Post-Stroke Survivors with a Diagnostic Approach

2020

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Stroke is considered as a major cause of death and neurological disorders commonly associated with elderly people. Electrocardiogram (ECG) signals are used as a powerful tool in diagnosing stroke, and the analysis of ECG signals has become the focus of stroke research. ECG changes and autonomic dysfunction are reportedly seen in patients with stroke. This study aimed to analyze the ECG features and develop a classification model with highly ranked ECG features as input variables based on machine-learning techniques for diagnosing stroke disease. The study included 52 stroke patients (mean age 72.7 years, 63% male) and 80 control subjects (mean age 75.5 years, 39% male) for a total of 132 elderly subjects. Resting ECG signals in the lying down position are measured using the BIOPAC MP150 system. The ECG signals are denoised using the discrete wavelet transform (DWT) method, and the features such as heart rate variability (HRV), indices of time and spectral domains and statistical and impulsive metrics, in addition to fiducial features, are extracted and analyzed. Our results showed that the values of the HRV variables were lower in the stroke group, revealing autonomic dysfunction in stroke patients. A statistically significant difference was observed in low-frequency (LF)/high-frequency (HF), time interval measured after the S wave to the beginning of the T wave (ST) and time interval measured from the beginning of the Q wave to the end of the T wave (QT) (p < 0.05) between the groups. Our study also highlighted some of the risk factors of stroke, such as age, male sex and dyslipidemia (p < 0.05), that are statistically significant. The k-nearest neighbors (KNN) model showed the highest classification results (accuracy 96.6%, precision 94.3%, recall 99.1% and F1-score 96.6%) than the random forest, support vector machine (SVM), Naïve Bayes and logistic regression models. Thus, our study reported some of the notable ECG changes in the study participants and also indicated that ECG could aid in diagnosing stroke disease.

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The Use of Deep Learning to Predict Stroke Patient Mortality

Cited by 108 publications

References 51 publications

The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model

The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model

An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

Evaluation of ECG Features for the Classification of Post-Stroke Survivors with a Diagnostic Approach

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