Analyzing the Performance of Stroke Prediction using ML Classification Algorithms

Sailasya, Gangavarapu; Kumari, Gorli L Aruna

doi:10.14569/ijacsa.2021.0120662

Cited by 125 publications

(61 citation statements)

References 8 publications

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“…In Table 3, the outcomes of the current research work are compared with the research study in [35] under the same dataset [34]. In relation to [35], all suggested models, especially DT and RF, significantly outperform their performance, in terms of recall, F-measure and accuracy. In conclusion, the stacking method remains the best performing method and the main suggestion of our study.…”

Section: Discussionmentioning

confidence: 99%

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Stroke Risk Prediction with Machine Learning Techniques

Δρίτσας

Trigka

2022

Sensors

136

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A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.

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Section: Discussionmentioning

confidence: 99%

“…Moreover, the Kaggle dataset [34] is applied in [35]. This research work suggests the implementation of various machine learning algorithms, such as logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine and naive Bayes.…”

Section: Related Workmentioning

confidence: 99%

Stroke Risk Prediction with Machine Learning Techniques

Δρίτσας

Trigka

2022

Sensors

136

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“…This includes the use of AI to analyze electrocardiogram and ultrasound data for risk stratification and projection of stroke outcomes in patients with known risk factors and to aid with stroke diagnosis using imaging data ( 46 ). Sailasya et al describe the performance of six classification-based MLAs to predict stroke, with the decision-tree model yielding the lowest performance and the Naïve Bayes model yielding the best performance (receiver operating curves 0.66 and 0.82, respectively) ( 47 ). A 2019 study by Li et al examined the use of ML for the purpose of filling in gaps in data that were collected as part of China's national stroke screening and prevention program ( 48 ).…”

Section: Discussionmentioning

confidence: 99%

Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm

et al. 2022

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BackgroundStrokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety.MethodsA retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke.ResultsAfter training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure.ConclusionMLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.

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“…Nnamoko and Korkontzelos [23] focused on this scheme and considered neural network based automated approach for monitoring these levels for type -1 diabetes patients. Sailasya et al [24] proposed a analyzing the performance of stroke prediction using ML classification algorithms moreover, supervised back propagation is applied on the training dataset to fine tune the network. Finally, softmax layer classifies the data corresponding to their classes.…”

Section: Background Workmentioning

confidence: 99%

A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory

Kumari

Padmaja²,

Suma

2022

IJEECS

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Hyperglycemia arises due to diabetes mellitus, which is a persistent and life-threatening ailment. In this paper deep convolution neural network can be embedded to long short-term memory networks to recognize early prediction of diabetes and to decrease the complications that can be occurred through diabetes irrespective to the age. Diabetes problem is being gradually growing and presently, it is reported as a significant cause of death in the top spot. According to the recent studies 48% of overall world population will be affected by diabetes by 2045. If diabetes unidentified in early stages, it may cause other additional cardiac problems. In the proposed based work, a deep learning framework deep combination of convolution neural network and long short-term memory is proposed by embedding both to leverage their respective advantages for diabetes recognition and to allow early prediction of diabetes to avoid other complications. The experimental evolution on the bunch mark of diabetes data set demonstrates the proposed model embedded deep long short-term memory outperforms other machine learning and conventional deep learning approaches. The proposed algorithm in this paper outperforms existing techniques and evaluates total effectiveness and accuracy of predicting whether a person will suffer from diabetes.

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Analyzing the Performance of Stroke Prediction using ML Classification Algorithms

Cited by 125 publications

References 8 publications

Stroke Risk Prediction with Machine Learning Techniques

Stroke Risk Prediction with Machine Learning Techniques

Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm

A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory

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