Authors :
Dr. M. Sindhuja; Vivek Kumar; Shivam Singh
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
http://tinyurl.com/ymah3ex2
Scribd :
http://tinyurl.com/rjaankfc
DOI :
https://doi.org/10.5281/zenodo.10663486
Abstract :
Air This study aims to tackle the increasing
prevalence of strokes in young adults by utilizing state-of-
the-art machine learning methods for predictive modeling.
Contrary to commonly held beliefs that strokes mostly
affect older individuals, there is a noticeable change in the
demographics, which requires the development of new
and creative approaches for detecting and intervening in
the early stages. Machine learning, a powerful technique
in the field of artificial intelligence, is on the verge of
transforming stroke prediction by integrating various
datasets that include medical records, lifestyle factors, and
genetic information. The resulting prediction model aims
to uncover intricate patterns and unique risk variables
related to young adults, offering a comprehensive insight
that goes beyond traditional risk assessments. The main
goal is to create an advanced prediction model that allows
for the early detection of persons with a high risk of
strokes. This would enable prompt and individualized
treatments to reduce the impact of strokes in this
unforeseen and vulnerable population. This research aims
to provide significant insights into preventative
healthcare, promoting a proactive approach to tackling
the specific issues presented by strokes in young adults.
Air This study aims to tackle the increasing
prevalence of strokes in young adults by utilizing state-of-
the-art machine learning methods for predictive modeling.
Contrary to commonly held beliefs that strokes mostly
affect older individuals, there is a noticeable change in the
demographics, which requires the development of new
and creative approaches for detecting and intervening in
the early stages. Machine learning, a powerful technique
in the field of artificial intelligence, is on the verge of
transforming stroke prediction by integrating various
datasets that include medical records, lifestyle factors, and
genetic information. The resulting prediction model aims
to uncover intricate patterns and unique risk variables
related to young adults, offering a comprehensive insight
that goes beyond traditional risk assessments. The main
goal is to create an advanced prediction model that allows
for the early detection of persons with a high risk of
strokes. This would enable prompt and individualized
treatments to reduce the impact of strokes in this
unforeseen and vulnerable population. This research aims
to provide significant insights into preventative
healthcare, promoting a proactive approach to tackling
the specific issues presented by strokes in young adults.
