A Machine Learning Approach to Detect the Brain Stroke Disease

Akter, Bonna; Rajbongshi, Aditya; Sazzad, Sadia; Shakil, Rashiduzzaman; Biswas, Jahanur; Sara, Umme

doi:10.1109/icssit53264.2022.9716345

Cited by 29 publications

(5 citation statements)

References 10 publications

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“…In 2022, [22] developed the model, and a "brain stroke dataset" was utilized. Data can be standardized through the use of a procedure called standardization.…”

Section: B State-of-the-art Prediction Of Ischemic Stroke Approachesmentioning

confidence: 99%

An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke

Sakri,

Basheer,

Zain

et al. 2024

IEEE Access

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Early detection of stroke warning symptoms can help reduce the severity of ischemic stroke, the leading cause of mortality and disability worldwide. This study aims to develop a model to predict the disease by leveraging machine learning-based models. A model that concatenates a convolutional neural network and a long short-term memory was developed as the proposed model. Seven other classifiers were treated as the baseline models: logistic regression, random forest, extreme gradient boosting, k-nearest neighbor, artificial neural network, long short-term memory, and convolutional neural network. All models were trained using a healthcare dataset of 5110 patients' health profiles. A synthetic minority oversampling technique was deployed to balance the data. Metrics such as accuracy, precision, F1-score, recall, area under the curve, and confusion metrics were used to evaluate the models' performance. With a 95.9% accuracy, the proposed model outperformed the models employed in this study and improved the accuracy of prior studies that used the same dataset. The Shapley Additive Explanations method was applied to explain the result obtained by the best model. The proposed model was created to predict ischemic stroke. It considers each patient's profile, allowing for personalized decision-making in resource-constrained settings.INDEX TERMS Ischemic stroke prediction, SHAP method, hybrid deep learning model, machine learning

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“…In 2022, [22] developed the model, and a "brain stroke dataset" was utilized. Data can be standardized through the use of a procedure called standardization.…”

Section: B State-of-the-art Prediction Of Ischemic Stroke Approachesmentioning

confidence: 99%

An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke

Sakri,

Basheer,

Zain

et al. 2024

IEEE Access

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“…The highest accuracy of 96% was attained by random forest across all performance metrics. Akter et al [34] proposed a stroke prediction model with acceptable accuracy. Popular machine learning algorithms like Random Forest, Support Vector Machine, and Decision Tree were developed using their model.…”

Section: Sailasya and Kumarimentioning

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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“…AI algorithms are essential for improving clinical trial design, patient recruitment, and data analysis in stroke research (Akter et al, 2022). Machine learning models examine various data sources such as electronic health records, medical imaging, genomics, and wearable sensor data to identify suitable patients, forecast trial results, and enhance trial procedures.…”

Section: Introductionmentioning

confidence: 99%

“…One of the leading causes of death and disability globally is a cerebrovascular accident, which is commonly referred to as a BS. The expenditures related to long-term care, rehabilitation, and medical treatments substantially load healthcare systems (Akter et al, 2022). Timely identification and management are vital in minimizing the consequences of cerebral strokes.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning-based Brain Stroke Prediction Model Using Magnetic Resonance Imaging

Abulfaraj,

Dutta,

Sait

2024

Journal of Disability Research

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Brain stroke (BS) imposes a substantial burden on healthcare systems due to the long-term care and high expenditure. Earlier detection and intervention can reduce the impact of BS. Magnetic resonance imaging (MRI) is commonly applied for BS detection. Deep learning techniques can employ MRI images to identify the BS risks in the initial stages. This study developed a BS detection model using an ensemble learning approach that combines the predictions of the base models. A MobileNet V3 model backbone was used to extract the intricate patterns of BS from MRI images. LightGBM and CatBoost models were used as base models to predict BS using the extracted features. In addition, the random forest model was used to integrate the predictions of base models to identify BS. The proposed model was generalized on a public MRI dataset that covers 2888 clinical MRI images. The experimental outcomes showed the effectiveness of the suggested BS detection model. The proposed model has obtained an accuracy of 98.7%, an area under the receiver operating characteristic score of 0.95, and an area under the precision–recall curve of 0.92. The recommended model is believed to be deployed in real-time healthcare settings to assist radiologists and clinicians in making effective decisions.

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A Machine Learning Approach to Detect the Brain Stroke Disease

Cited by 29 publications

References 10 publications

An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke

An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke

A Machine Learning Ensemble Classifier for Prediction of Brain Strokes

Ensemble Learning-based Brain Stroke Prediction Model Using Magnetic Resonance Imaging

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