• The Journal of the Korea Contents Association
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• v.17 no.10
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• pp.94-101
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• 2017

This study is to compare and analyze the bioelectrical impedance values on the upper extremity (affected and non-affected side) in hemiplegic stroke patients. Experimental subjects were 24 stroke patients with hemiplegia undergoing stroke rehabilitation between October to November, 2015. Prediction marker, resistance, reactance, and phase angle were measured in the upper extremity (affected and non-affected side) of hemiplegic stroke patients, using MultiScan 5000, and then they were expressed as quantified values. The affected and non-affected side of upper extremity in stroke patients with hemiplegia exhibited significant differences in prediction marker, reactance, and phase angle (p<0.05). There were significant differences in the impedance values at the affected and non-affected side of hemiplegic stroke patients. Thus, the possibility of evaluating stroke patients undergoing clinical rehabilitation therapy was suggested.

• Korean Journal of Artificial Intelligence
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• v.12 no.2
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• pp.1-7
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• 2024

In this paper, we compare three models (logistic regression, Random Forest, and XGBoost) for predicting stroke occurrence using data from the Korea National Health and Nutrition Examination Survey (KNHANES). We evaluated these models using various metrics, focusing mainly on recall and F1 score to assess their performance. Initially, the logistic regression model showed a satisfactory recall score among the three models; however, it was excluded from further consideration because it did not meet the F1 score threshold, which was set at a minimum of 0.5. The F1 score is crucial as it considers both precision and recall, providing a balanced measure of a model's accuracy. Among the models that met the criteria, XGBoost showed the highest recall rate and showed excellent performance in stroke prediction. In particular, XGBoost shows strong performance not only in recall, but also in F1 score and AUC, so it should be considered the optimal algorithm for predicting stroke occurrence. This study determines that the performance of XGBoost is optimal in the field of stroke prediction.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.5
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• pp.637-653
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• 1999

In this study well-known burned rate expressions of Weibe function and double Wiebe function have been adopted for the combustion analysis of large two stroke marine diesel engine. A cycle simulation program was also developed to predict the performance and pressure waves in pipes using validated burned rate function,. Levenberg-Marquardt iteration method was applied to cali-brate the shape coefficients included in double Wiebe function for the performance prediction of two-stroke marine diesel engine. As a result the performance prediction using double Wiebe func-tion is well correlated withexperimental dta with the accuracy of 5% and pressure waves in intake and transport pipe are well predicted. From the results of this study it can be confirmed that the shape coefficients of burned rate function should be modified using the numerical method suggested for the accurated prediction and double Wiebe function is more suitable than Wiebe func-tion for combustion analysis of large two stroke marine engine.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.6
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• pp.1113-1118
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• 2011

In this paper, when a physician make a diagnosis of the pattern identification (PI) in Korean stroke patients, the development methods of the PI classification function is considered by diagnostic questionnaire of the PI for stroke patients. Clinical data collected from 1,502 stroke patients who was identically diagnosed for the PI subtypes diagnosed by two physicians with more than 3 years experiences in 13 oriental medical hospitals. In order to develop the classification function into PI using Korean Stroke Syndrome Differentiation Standard was consist of the 44 items (Fire heat(19), Qi deficiency(11), Yin deficiency(7), Dampness-phlegm(7)). Using the 44 items, we took diagnostic and prediction accuracy rate through of discriminant model. The overall diagnostic and prediction accuracy rate of the PI subtypes for discriminant model was 74.37%, 70.88% respectively.

• Physical Therapy Korea
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• v.17 no.3
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• pp.59-67
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• 2010

Mortality rates from stroke have been declining. Because of this, more people are living with residual disability. Rehabilitation plays an important role in functional recovery of stroke survivors. In stroke rehabilitation, early prediction of the obtainable level of functional recovery is desirable to deliver efficient care, set realistic goals, and provide appropriate discharge planning. The purpose of this study was to identify predictors of functional outcome after stroke using inpatient rehabilitation as measured by Functional Independence Measure (FIM) total scores. Correlation and stepwise multiple regression analyses were performed on data collected retrospectively from two-hundred thirty-five patients. More than moderate correlation was found between FIM total scores at the time of hospital admission and FIM total scores at the time of discharge from the hospital. Significant predictors of FIM at the time of discharge were FIM total scores at the time of hospital admission, age, and onset-admission interval. The equation was as follows: expected discharge FIM total score = $76.12+.62{\times}$(admission FIM total score)-$.38{\times}(age)-.15{\times}$(onset-admission interval). These findings suggest that FIM total scores at the time of hospital admission, age, and onset-admission interval are important determinants of functional outcome.

• ETRI Journal
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• v.45 no.6
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• pp.1007-1021
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• 2023

Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795 000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self-training technique to predict the risk of stroke. We then consider the near-miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.

• Journal of Preventive Medicine and Public Health
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• v.53 no.5
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• pp.313-322
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• 2020

Objectives: This study was conducted to determine the incidence and risk factors of myocardial infarction (MI) and stroke in farmers compared to the general population and to establish 5-year prediction models. Methods: The farmer cohort and the control cohort were generated using the customized database of the National Health Insurance Service of Korea database and the National Sample Cohort, respectively. The participants were followed from the day of the index general health examination until the events of MI, stroke, or death (up to 5 years). Results: In total, 734 744 participants from the farmer cohort and 238 311 from the control cohort aged between 40 and 70 were included. The age-adjusted incidence of MI was 0.766 and 0.585 per 1000 person-years in the farmer and control cohorts, respectively. That of stroke was 0.559 and 0.321 per 1000 person-years in both cohorts, respectively. In farmers, the risk factors for MI included male sex, age, personal history of hypertension, diabetes, current smoking, creatinine, metabolic syndrome components (blood pressure, triglycerides, and high-density lipoprotein cholesterol). Those for stroke included male sex, age, personal history of hypertension, diabetes, current smoking, high γ-glutamyl transferase, and metabolic syndrome components (blood pressure, triglycerides, and high-density lipoprotein cholesterol). The prediction model showed an area under the receiver operating characteristic curve of 0.735 and 0.760 for MI and stroke, respectively, in the farmer cohort. Conclusions: Farmers had a higher age-adjusted incidence of MI and stroke. They also showed distinct patterns in cardiovascular risk factors compared to the general population.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.183-189
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• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.55-64
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• 2019

Stroke is a health problem experienced by many elderly people around the world. Stroke has a devastating effect on quality of life, causing death or disability. Hemiplegia is clearly an early sign of a stroke and can be detected through patterns of body balance and gait. The goal of this study was to determine various feature vectors of foot pressure and gait parameters of patients with stroke through the use of a wearable sensor and to compare the gait parameters with those of healthy elderly people. To monitor the participants at all times, we used a simple measuring device rather than a medical device. We measured gait data of 220 healthy people older than 65 years of age and of 63 elderly patients who had experienced stroke less than 6 months earlier. The center of pressure and the acceleration during standing and gait-related tasks were recorded by a wearable insole sensor worn by the participants. Both the average acceleration and the maximum acceleration were significantly higher in the healthy participants (p < .01) than in the patients with stroke. Thus gait parameters are helpful for determining whether they are patients with stroke or normal elderly people.

• Physical Therapy Korea
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• v.14 no.3
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• pp.81-89
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• 2007

The purpose of this study was to utilize the K-MBI (Korean Modified Barthel Index) and subscales of K-MBI in predicting the length of hospital stay (LOS) and the discharge destinations for stroke patients. The study population consisted of 97 stroke patients (57 men and 40 women) admitted to the Seoul National University at the Bundang Hospital. All participants were assessed by K-MBI at admission and discharge after rehabilitation therapy and the information available was investigated at admission. The data were analyzed by using the Mann-Whitney U test, the stepwise multiple regression and the logistic regression. The median LOS was 30 days (mean, 32.8 days; range, 22 to 43 days). The K-MBI score at initiation of rehabilitation therapy (p<.001), the type of stroke and living habits before a stroke were the main explanatory indicators for LOS (p<.05). Within the parameters of K-MBI measured at initiation for rehabilitation, feeding and chair/bed transfer were the explanatory factors for LOS prediction (p<.01). Confidence in the prediction of LOS was 20%. Significant predictors of discharge destination in a logistic regression model were the discharge K-MBI score, sex and hemiplegic side. Dressing in items of discharge K-MBI was the significant predictor of discharge destination. The K-MBI score was the most important factor to predict LOS and discharge destination. Knowledge of these predictors can contribute to more appropriate treatment and discharge planning.