An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach

Abstract

Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.

Deep Transfer Learning Based Stroke Risk Prediction

“…The proposed approach automates stroke prediction using AI or machine learning methods. [4] A problem of great medical relevance was examined to see if deep neural network models could predict ischemic stroke. The first trials produced great results on the test set and were encouraging.Due to the fact that stroke-positive pictures come from a single source, however, dataset ablation and feature separation via vascular tree extraction were attempts to measure the impact of these parameters on performance.…”

Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis

“…Early detection of stroke is a crucial step in ensuring effective treatment and ML has demonstrated significant value in facilitating this process [ 18 ]. Numerous applications of ML/DL in stroke have been reported, such as brainwaves being investigated for stroke prediction [ 19 ], electroencephalography (EEG) signals utilized to develop explainable AI models for stroke prediction [ 20 ], EEG features used for quantitative evaluation of task-induced neurological outcome after stroke [ 21 ], electrocardiogram (ECG) used to identify atrial fibrillation (AF) related stroke [ 22 ], electromyography (EMG) applied for prediction of myoelectric biomarkers in post-stroke gait [ 23 ], biosignals being investigated for stroke prediction [ 24 ], among others. Moreover, some ML models have been developed into automated applications for various clinical tasks, including identifying large vessel occlusions (LVOs), diagnosing ischemic and hemorrhagic stroke, and assessing salvageable brain tissue [ 25 ].…”

“…The prediction of large vessel occlusion for ischemic stroke has been accomplished using the RF algorithm in an ML model [ 12 ]. Additionally, an ML model constructed using the SVM algorithm has been trained for stroke prediction [ 24 ]. Moreover, in a study predicting aneurysmal subarachnoid hemorrhage, seven ML models, including LR, SVM, DT, and DNN, were employed [ 27 ].…”

Predictive modelling and identification of key risk factors for stroke using machine learning

“…In the field of medicine, machine learning has become a powerful technology that has the potential to transform stroke prevention and prediction 5 – 7 . Machine learning models use large datasets and sophisticated algorithms to identify hidden risk factors, forecast outcomes, and offer tailored strategies for treatment 8 .…”