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Nevithaprakasini, M.. Risk detection of stroke using classifier algorithms. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2022.
Risk detection of stroke using classifier algorithms
Name: Ponselvakumar, A.P.
Organization :
Kongu Engineering College. Department of Information Technology Information
Email :
ponselva.it@kongu.edu
Name: Nivetha, S.
Organization :
Kongu Engineering College. Department of Information Technology Information
Email :
nivethasubraj2000@gmail.com
Name: Nevithaprakasini, M.
Organization :
Kongu Engineering College. Department of Information Technology Information
Email :
nevithaprakasini@gmail.com
keyword:
Jupyter
LCSH:
Machine learning
LCSH:
Decision trees
LCSH:
Cerebrovascular disease
Abstract:
Today's healthcare is very
important aspect for every human, so there is
a need to provide medical services that are
easily available to everyone. The primary goal
is to use machine learning techniques to
detect stroke risk.. Stroke is a condition that
damages the brain's arteries. Various risk
factors have been linked to the start of stroke
in an individual, according to studies. Using
patient medical information, data mining
techniques were applied to forecast the
occurrence of stroke based on these
parameters. However, the use of electronic
health data to explore the interrelation of
diverse stroke risk factors has been limited.
This study examines patient electronic health
records to see how risk factors affect stroke
prediction. It is possible to compare the
performance of machine learning algorithms
for detecting stroke using healthcare health
data. The suggested algorithm's knowledge
has a good classification accuracy and the
capacity to detect stroke risk. After choosing
the dataset, look at a subset of variables to see
if it's possible to address the problem for
which the discovery is being made. Clean the
data for the training set in order to locate
usable features to represent the data based on
the task's purpose. The Random Forest
method is deployed using the Flask micro
framework, which improves classification
accuracy.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2022
Modified:
2024-05-13
Issued:
2024-05-13
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Computer Society. 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT 2022) (pp.19-24). Los Alamitos, CA : IEEE Computer Society
eng
©copyrights King Mongkut's University of Technology North Bangkok
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ใช้เวลา
0.039317 วินาที
Ponselvakumar, A.P.
| Title | Contributor | Type |
|---|---|---|
| Risk detection of stroke using classifier algorithms
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Ponselvakumar, A.P.;Nivetha, S.;Nevithaprakasini, M. | บทความ/Article |
Nivetha, S.
| Title | Contributor | Type |
|---|---|---|
| Risk detection of stroke using classifier algorithms
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Ponselvakumar, A.P.;Nivetha, S.;Nevithaprakasini, M. | บทความ/Article |
Nevithaprakasini, M.
| Title | Contributor | Type |
|---|---|---|
| Risk detection of stroke using classifier algorithms
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Ponselvakumar, A.P.;Nivetha, S.;Nevithaprakasini, M. | บทความ/Article |