| Issue |
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
|
|
|---|---|---|
| Article Number | 01020 | |
| Number of page(s) | 5 | |
| DOI | https://doi.org/10.1051/itmconf/20257901020 | |
| Published online | 08 October 2025 | |
Machine Learning Algorithm to Detect Neurological Disorder
Department of Electronics and Instrumentation Engineering, Ramaiah Institute of Technology, Bengaluru, India
* Corresponding author: jvalamelu@msrit.edu
Abnormalities of brain is the main concern for healthcare industries, if it is not recognized or treated may lead to sever brain death. Neurological disorder such as epilepsy is slower and requires constant diagnosis and treatment. With the advent of recent technology physiologists are using x-rays, EEG, CT and MRI for early detection of such disorders. The diagnosis is accomplished by implementing machine learning techniques which aid in proper treatment. In this paper signals are acquired using electroencephalogram, a technique, that consists of parameters to classify different neurological disorders such as anxiety, epilepsy spikes etc., For this purpose, Machine Learning algorithms is implemented to categorize, which performs the classification of different brain abnormalities especially on epilepsy. Accuracy seems to be 96.1 under Extra Tree Classifier. This act as the assisting platform for doctors, aimed at effective treatment.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.