The early diagnosis of neurological
problems in children helps medical personnel to enhance the patients' health.
Therefore, it is essential to recognise neurological anomalies since, if
treatment is delayed, they might turn into major problems. Medical data may be
analysed and the problem can be accurately diagnosed with the help of machine
learning algorithms. This research has discovered machine learning algorithms
on several accuracy metrics to accurately detect three prevalent neurological
disorders. A neurological data set is collected from a neuro clinic facility in
order to evaluate the efficacy of machine learning approaches. Numerous
psychological examinations, including clinic neuropsychiatric observation,
audio evaluation, and intellectual coefficient assessment, are also carried out
on people who have neurological diseases. Some of the collected characteristics
were found to be crucial for figuring out the problem. The findings
unmistakably demonstrate that the chosen ML techniques produced results that
were more accurate, and there is just a little variation in how well they
performed.
Author(s) Details:
G. Reshma,
Department of Information Technology, PVPSIT, Kanuru, Vijayawada, India.
P. V. S Lakshmi,
Department of
Information Technology, PVPSIT, Kanuru, Vijayawada, India.
Please see the link here: https://stm.bookpi.org/RDST-V10/article/view/7723
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