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

• Published in: Computational intelligence and neuroscience 2022-10, Vol.2022, p.1-9
• Main Authors: Pitchai, R., Dappuri, Bhasker, Pramila, P. V., Vidhyalakshmi, M., Shanthi, S., Alonazi, Wadi B., Almutairi, Khalid M. A., Sundaram, R. S., Beyene, Ibsa
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Big Data; Brain damage; Brain research; Classifiers; Clinical trials; Data mining; Disabilities; Disease; Electromyography; Epidemics; Fatalities; Forecasts and trends; Health care; Illnesses; Image analysis; Image processing; Internet of Things; Learning algorithms; Machine learning; Medical imaging; Medical records; Medical technology; Neural networks; Older people; Personal health; Predictions; Real time; Risk factors; Simulation methods; Stroke; Support vector machines; Training; Well being; Working hours
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/5489084

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.