Abstract
This study explores a machine learning-based neural network system that uses MATLAB to classify hemiplegia, a disease that causes paralysis on one side of the body. An algorithm was developed to categorize patients into four established types of hemiplegia. Techniques such as Principal Component Analysis (PCA) and Self-Organizing Maps (SOMs) were used for dimensionality reduction and data clustering, while a Convolutional Neural Network (CNN) refined the classification. The algorithm identified distinct subgroups within the categories, indicating a more complex data structure. Despite promising results to aid clinical diagnosis, further exploration of these subcategories is needed.
References
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