Prediction of Stroke using Data Mining Classification  
Techniques

Brain Strokes are one of the most serious and most common diseases in the world due to their sudden occurrence. It can be considered the death of brain cells as a result of the failure to provide these cells with the right amount of blood, which leads to the interruption of the brain's action and therefore leads to death within minutes. Therefore, predicting the incidence of strokes based on the risk factors of the patient is one of the most important reasons for preventing the occurrence of these strokes and providing early treatment for them. Machine learning techniques are widely used in building predictive models for strokes according to the patient's electronic health record, which contains the factors that lead to the occurrence of stroke. Ensemble methods are one of the most important concepts in machine learning as it works to collect more than one machine learning algorithm and combine them to produce a reliable predictive model with higher accuracy. The purpose of this paper is to present a survey on predictive models for Brain Strokes using a machine learning ensemble classifier.

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“…Almadani et al [25] this study focused on developing a model for stroke prediction using data mining techniques. They used J84 (C4.…”

Section: Ensemble Methods For Brain Stroke Predictionmentioning

confidence: 99%

نماذج تعلم الآلة للتنبؤ بالسکتات الدماغیة

ابراهیم¹,

الزنفلی²,

یعقوب³

2022

النشرة المعلوماتیة فی الحاسبات والمعلومات

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“…Almadani and Alshammari [23] proposed a stroke prediction model using J48, Jrip, and neural networks (multilayer recognition). The model was built using datasets from the data management department of King Abdulaziz Medical City, Saudi Arabia.…”

Section: Singh and Choudharymentioning

confidence: 99%

A Machine Learning Ensemble Classifier for Prediction of Brain Strokes

Mostafa¹,

Elzanfaly²,

Yakoub³

2022

IJACSA

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Brain Strokes are considered one of the deadliest brain diseases due to their sudden occurrence, so predicting their occurrence and treating the factors may reduce their risk. This paper aimed to propose a brain stroke prediction model using machine learning classifiers and a stacking ensemble classifier. The smote technique was employed for data balancing, and the standardization technique was for data scaling. The classifiers' best parameters were chosen using the hyperparameter tuning technique. The proposed stacking prediction model was created by combining Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes (NB) as base classifiers, and meta learner was chosen to be Random Forest. The performance of the proposed stacking model has been evaluated using Accuracy, Precision, Recall, and F1 score. In addition, the Matthews Correlation Coefficient (MCC) has been also used for more reliable evaluation when having an unbalanced dataset, which is the case in most medical datasets. The results demonstrate that the proposed stacking model outperforms the standalone classifiers by achieving an accuracy of 97% and an MCC value of 94%.

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“…In [9], the authors have used different data mining classification techniques to predict the possibility of a stroke. The dataset was taken from the Ministry of National Guards Health Affairs Hospitals, Kingdom of Saudi Arabia.…”

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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Prediction of Stroke using Data Mining Classification Techniques

Cited by 21 publications

References 7 publications

نماذج تعلم الآلة للتنبؤ بالسکتات الدماغیة

نماذج تعلم الآلة للتنبؤ بالسکتات الدماغیة

A Machine Learning Ensemble Classifier for Prediction of Brain Strokes

Analyzing the Performance of Stroke Prediction using ML Classification Algorithms

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