Stroke Prediction Using Machine Learning Classification Methods

Al-Zubaidi, Hamza; Dweik, Mohammed; Al-Mousa, Amjed

doi:10.1109/acit57182.2022.10022050

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…The feature ranker with a low score algorithm was used to determine the impact of two features. By utilising various machine learning classification techniques, Hamza et al [63] attempted to create a supervised model that could forecast the presence of a stroke in the near future depending on specific criteria. SVM, decision trees, random forests, and logistic regression were applied in this study.…”

Section: Discussionmentioning

confidence: 99%

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Chadaga,

Sampathila

et al. 2023

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Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Stroke is a common cause of mortality among older people. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Healthcare professionals can discover solutions more quickly and accurately using artificial intelligence (AI) and machine learning (ML). As a result, we have shown how to predict stroke in patients using heterogeneous classifiers and explainable artificial intelligence (XAI). The multistack of ML models surpassed all other classifiers, with accuracy, recall, and precision of 96%, 96%, and 96%, respectively. Explainable artificial intelligence is a collection of frameworks and tools that aid in understanding and interpreting predictions provided by machine learning algorithms. Five diverse XAI methods, such as Shapley Additive Values (SHAP), ELI5, QLattice, Local Interpretable Model-agnostic Explanations (LIME) and Anchor, have been used to decipher the model predictions. This research aims to enable healthcare professionals to provide patients with more personalized and efficient care, while also providing a screening architecture with automated tools that can be used to revolutionize stroke prevention and treatment.

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Section: Discussionmentioning

confidence: 99%

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Chadaga,

Sampathila

et al. 2023

Information

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“…According to the experiments, the proposed model using artificial neural network (ANN) gave the best performance with a receiver operating characteristics curve (ROC) score of 0.84 compared to other studies. Al-Zubaidi et al (2022) have proposed a supervised model using different machine-learning classification methods. The SMOTE technique is utilized for the purpose of rebalancing the imbalanced stroke dataset, while the mean simple method is used to impute the missing values within the dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Al‐Zubaidi et al (2022) have proposed a supervised model using different machine‐learning classification methods. The SMOTE technique is utilized for the purpose of rebalancing the imbalanced stroke dataset, while the mean simple method is used to impute the missing values within the dataset.…”

Section: Related Workmentioning

confidence: 99%

A heuristic‐based hybrid sampling method using a combination of SMOTE and ENN for imbalanced health data

Nizam‐Ozogur,

Orman

2024

Expert Systems

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Health datasets often exhibit class imbalance, with healthy individuals being the majority class and patients being the minority class. As in many applications, machine learning methods are frequently used in the prediction and detection of diseases. The class imbalance presents a significant challenge for machine learning classifiers, particularly in their ability to accurately and effectively classify data from minority and majority classes. Therefore, data preprocessing is crucial before classifying the imbalanced data. In this study, we present GASMOTEPSO_ENN method that combines the synthetic minority oversampling technique (SMOTE) and edited nearest neighbour (ENN) algorithms using genetic algorithm (GA) and particle swarm optimization (PSO) heuristics as a preprocessing method to classify the imbalanced health datasets. In the experiments, chronic kidney disease (CKD), cerebral stroke prediction (CSP), and PIMA Indian diabetes (PID) datasets were utilized to assess the performance of the proposed method with metrics derived from the confusion matrix. The GASMOTEPSO_ENN method can classify the various diseases into different two classes of patients and healthy individuals with acceptable Matthews correlation coefficient (MCC) metric using the machine learning algorithms (Logistic regression (LR) 1.00 for CKD dataset, extreme gradient boosting (XGBoost) 0.94 for CSP dataset, and support vector machine (SVM) 0.87 for PID dataset). Moreover, the proposed method also performed well with other metrics in all datasets, and the analysis of the model results in relation to existing literature reveals that the proposed model demonstrably produces superior results.

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“…For example, in the prediction of multiple diseases like diabetes, 9 heart disease, 10 , 11 and strokes. 12 As for ECG heartbeat classification, several approaches have been taken regarding signal preprocessing and various models and implementations. Wang et al 13 have used the Easy Ensemble technique and global heartbeat information for an imbalanced heartbeat classification on the MIT-BIH arrhythmia dataset.…”

Section: Related Workmentioning

confidence: 99%

Enhanced electrocardiogram machine learning-based classification with emphasis on fusion and unknown heartbeat classes

Al-mousa,

Baniissa,

Hashem

et al. 2023

DIGITAL HEALTH

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Building an electrocardiogram (ECG) heartbeat classification model is essential for early arrhythmia detection. This research aims to build a reliable model that can classify heartbeats into five heartbeat types: normal beat (N), supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), fusion beat (F), and unknown beat (Q), with a focus on enhancing the predictions of the uncommon Q and F heartbeats. The base dataset used is the MIT-BIH SupraVentricular Database, which was used to train and compare the performance of five machine learning models: logistic regression, Random Forest (RF), K-nearest neighbor, linear support vector machine, and linear discriminant analysis. In addition to using the synthetic minority oversampling technique, data extracted from multiple databases for the F and Q classes were combined with the original base dataset. These methods resulted in significant improvement in the recall for the rare F and Q classes when compared to the literature. The RF algorithm produced the best performance with an accuracy of 97% and recall values equal to 97%, 93%, 95%, 95%, and 30% for N, SVEB, VEB, F, and Q, respectively.

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Stroke Prediction Using Machine Learning Classification Methods

Cited by 13 publications

References 7 publications

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

A heuristic‐based hybrid sampling method using a combination of SMOTE and ENN for imbalanced health data

Enhanced electrocardiogram machine learning-based classification with emphasis on fusion and unknown heartbeat classes

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