Abstract:
Ischemic stroke is a type of stroke disease that appears due to a violation of
blood circulation in the blood vessels of the brain. People aged 40-60 are
highly affected, and men are more significantly impacted due to lifestyle
factors such as smoking, unhealthy eating habits, and excessive alcohol
consumption. Ayurvedic treatment emphasizes a balanced lifestyle, natural
therapies, and dietary interventions, which are considered a beneficial
traditional approach for stroke recovery. Collecting patient data and
analyzing it to determine their health status requires a significant amount of
time. To address this issue, a machine learning approach using a decision tree
model has been developed to predict recovery status in ischemic stroke
patients following Ayurvedic treatment. This model helps healthcare
professionals assess patients' post-treatment conditions more efficiently. The
data collection process involved gathering patient records from neurology
and Ayurvedic treatment units, including laboratory results, demographic
information, and treatment adherence data. Preprocessing steps, such as
handling missing values, feature selection, and normalization, were applied
to ensure the quality and usability of the data for the model. In conclusion, a
system is developed to analyze each patient's improvement level across
various conditions and predict their health status, specifically focusing on
identifying recovery levels, facilitating the inclusion of new data, and
providing a clear display of the analysis results.
