Stroke prediction using SVM

Jeena, R. S.; Kumar, Sukesh

doi:10.1109/iccicct.2016.7988020

Cited by 54 publications

(10 citation statements)

References 4 publications

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“…Jeena and Kumar [24] developed a stroke prediction model that predicts the probability of developing a stroke based on various risk factors. Model age, atrial fibrillation, gait symptoms, visual impairment, etc.…”

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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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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“…SVM is a supervised data mining algorithm for classification, prognosis and regression. This method works based on integration of linear algorithms and linear (or nonlinear) nuclear functions (Jeena et al, 2016). In this study, the 10-fold cross-validation technique was employed to conduct SVM.…”

Section: Model Constructionmentioning

confidence: 99%

Predicting Mortality in Patients with Stroke Using Data Mining Techniques

Hadianfard¹,

Afshar²,

Nazarbaghi³

et al. 2022

AIP

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The mortality due to stroke is increasing. Accurate prediction of stroke-caused death is very important for healthcare. Data mining methods are novel ways to predict these mortality risks. The aim of this study is to employ popular data mining algorithms to predict the survival of stroke patients and extract decision rules. The data on stroke patients (n=4149) were collected from paper medical records. Missing data were managed using the multiple imputation method. Also, the target variable was balanced using methods such as over-sampling, under-sampling and Synthetic Minority Oversampling (SMOTE). The support vector machine (SVM), decision tree, and logistic regression (LR) algorithms were employed to predict the survival of stroke patients. Also, the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was used to extract the decision rules from the main dataset. LR outperformed other algorithms in terms of accuracy (76.96%), sensitivity (79.06%) and kappa (33.34). However, specificity (65.35%) and AUC (0.77) scores were lower than those of other algorithms. An independent dataset with 234 records was selected to challenge the LR algorithm with the best performance from the main dataset. After employing this algorithm on the external validation dataset, its performance was improved in accuracy (79.91%), sensitivity (83.94%), kappa (39.26) and AUC (0.8), but not in specificity (60.98%). The constructed model predicted the survival of stroke patients with high scores and useful rules were extracted for clinical usage.

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“…Existing works in the literature have investigated various aspects of stroke prediction. Jeena et al provides a study of various risk factors to understand the probability of stroke [8]. It used a regression-based approach to identify the relation between a factor and its corresponding impact on stroke.…”

Section: Related Workmentioning

confidence: 99%

“…Several studies [4,5,6,7] have analysed the importance of lifestyle types, medical records of patients on the probability of the patients to develop stroke. Further, machine learning models are also now employed to predict the occurrence of stroke [8,9]. However, there is no study that attempts to analyse all the conditions related to patient, and identify the key factors necessary for stroke prediction.…”

Section: Introductionmentioning

confidence: 99%