Stroke prediction using artificial intelligence

Singh, Maninder; Choudhary, Prakash

doi:10.1109/iemecon.2017.8079581

Cited by 83 publications

(24 citation statements)

References 4 publications

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“…ey have 83 percent area under the curve (AUC). Singh et al [10] conducted research using artificial intelligence to predict strokes. ey employed a new technique for predicting stroke in their research using the cardiovascular health study (CHS) dataset.…”

Section: Introductionmentioning

confidence: 99%

Stroke Disease Detection and Prediction Using Robust Learning Approaches

Tazin

Alam

Dola

et al. 2021

Journal of Healthcare Engineering

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Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. According to the World Health Organization (WHO), stroke is the greatest cause of death and disability globally. Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke. Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain. This research uses a range of physiological parameters and machine learning algorithms, such as Logistic Regression (LR), Decision Tree (DT) Classification, Random Forest (RF) Classification, and Voting Classifier, to train four different models for reliable prediction. Random Forest was the best performing algorithm for this task with an accuracy of approximately 96 percent. The dataset used in the development of the method was the open-access Stroke Prediction dataset. The accuracy percentage of the models used in this investigation is significantly higher than that of previous studies, indicating that the models used in this investigation are more reliable. Numerous model comparisons have established their robustness, and the scheme can be deduced from the study analysis.

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Section: Introductionmentioning

confidence: 99%

Stroke Disease Detection and Prediction Using Robust Learning Approaches

Tazin

Alam

Dola

et al. 2021

Journal of Healthcare Engineering

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“…Min et al in [11] developed an algorithm for predicting stroke from potentially modifiable risk factors. Singh and Choudhary in [12] have used decision tree algorithm on Cardiovascular Health Study (CHS) dataset for predicting stroke in patients. A deep learning model based on a feed-forward multi-layer artificial neural network was also studied in [13] to predict stroke.…”

Section: Related Workmentioning

confidence: 99%

A predictive analytics approach for stroke prediction using machine learning and neural networks

Dev¹,

Wang²,

Nwosu³

et al. 2022

Preprint

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“…According to a recent research, data mining and machine learning methodologies have an important role in the diagnosis and prediction of diseases in the healthcare application [30][31][32][33][34][35]. Recently, various studies have been published on the prediction and analysis of brain diseases, using data mining and machine learning methodology [30][31][32][33][34]. A decision tree is a method of decision support tool that shows the various rules and their results in a tree structure.…”

Section: Machine Learning In Stroke Analysis and Disease Predictionmentioning

confidence: 99%

An Elderly Health Monitoring System Using Machine Learning and In-Depth Analysis Techniques on the NIH Stroke Scale

Park

Lee

et al. 2020

Mathematics

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Recently, with the rapid change to an aging society and the increased interest in healthcare, disease prediction and management through various healthcare devices and services is attracting much attention. In particular, stroke, represented by cerebrovascular disease, is a very dangerous disease, in which death or mental and physical aftereffects are very large in adults and the elderly. The sequelae of such stroke diseases are very dangerous, because they make social and economic activities difficult. In this paper, we propose a new system to prediction and in-depth analysis stroke severity of elderly over 65 years based on the National Institutes of Health Stroke Scale (NIHSS). In addition, we use the algorithm of decision tree of C4.5, which is a methodology of prediction and analysis of machine learning techniques. The C4.5 decision trees are machine learning algorithms that provide additional in-depth rules of the execution mechanism and semantic interpretation analysis. Finally, in this paper, it is verified that the C4.5 decision tree algorithm can be used to classify and predict stroke severity, and to obtain additional NIHSS features reduction effects. Therefore, during the operation of an actual system, the proposed model uses only 13 features out of the 18 stroke scale features, including age, so that it can provide faster and more accurate service support. Experimental results show that the system enables this by reducing the patient NIH stroke scale measurement time and making the operation more efficient, with an overall accuracy, using the C4.5 decision tree algorithm, of 91.11%.

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Stroke prediction using artificial intelligence

Cited by 83 publications

References 4 publications

Stroke Disease Detection and Prediction Using Robust Learning Approaches

Stroke Disease Detection and Prediction Using Robust Learning Approaches

A predictive analytics approach for stroke prediction using machine learning and neural networks

An Elderly Health Monitoring System Using Machine Learning and In-Depth Analysis Techniques on the NIH Stroke Scale

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