Artificial Intelligence for Medical Decisions

Buchard, Albert; Richens, Jonathan G.

doi:10.1007/978-3-030-58080-3_28-1

Cited by 1 publication

(1 citation statement)

References 95 publications

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“…Because of recent developments in computer infrastructure and the appearance of multiple AI frameworks in information and communication technology (ICT), AI-based digital healthcare analysis has recently become more complex and feasible. A new method called smart healthcare allows for the remote management of people's health by utilising ICT and large amounts of medical data [ 6 ].…”

Section: Introduction and Related Workmentioning

confidence: 99%

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

Pitchai¹,

Dappuri

Pramila

et al. 2022

Computational Intelligence and Neuroscience

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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.

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Section: Introduction and Related Workmentioning

confidence: 99%