Use of Machine Learning and Random OverSampling in Stroke Prediction

M. Sandeep Kini; Devidas; Smitha N. Pai; Sucheta Kolekar; Vasudeva Pai; R. Balasubramani

doi:10.1109/AIDE57180.2022.10060313

Use of Machine Learning and Random OverSampling in Stroke Prediction

M. Sandeep Kini
, Devidas
, Smitha N. Pai
, Sucheta Kolekar
, Vasudeva Pai
, R. Balasubramani

Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

A stroke is a medical condition where blood flow to the brain is interrupted. This is caused because of blood clots. Various factors, like hypertension, diabetes, heart disease, smoking, and body mass index level, result in a stroke. Almost every year, more than 15 million people have a stroke worldwide. Stroke is one of the major reasons of worldwide deaths. Hence prediction of a stroke becomes very crucial. This paper is focused on using various machine learning algorithms, namely, Support Vector Machine(SVM), Polynomial Kernel SVM, Sigmoid Kernel SVM, Gaussian Kernel SVM, Random Forest, and Logistic Regression. The paper also presents a comparison between different algorithms and accuracy scores. Random Forest gave the highest accuracy of 99.5%. Hence Random Forest is the best-suited model to predict Stroke.

Original language	English
Title of host publication	International Conference on Artificial Intelligence and Data Engineering, AIDE 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	331-337
Number of pages	7
ISBN (Electronic)	9781665493048
DOIs	https://doi.org/10.1109/AIDE57180.2022.10060313
Publication status	Published - 2022
Event	2022 International Conference on Artificial Intelligence and Data Engineering, AIDE 2022 - Karkala, India Duration: 22-12-2022 → 23-12-2022

Publication series

Name	International Conference on Artificial Intelligence and Data Engineering, AIDE 2022

Conference

Conference	2022 International Conference on Artificial Intelligence and Data Engineering, AIDE 2022
Country/Territory	India
City	Karkala
Period	22-12-22 → 23-12-22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems
Statistics, Probability and Uncertainty
Instrumentation
Artificial Intelligence

Access to Document

10.1109/AIDE57180.2022.10060313

Cite this

Sandeep Kini, M., Devidas, Pai, S. N., Kolekar, S., Pai, V., & Balasubramani, R. (2022). Use of Machine Learning and Random OverSampling in Stroke Prediction. In International Conference on Artificial Intelligence and Data Engineering, AIDE 2022 (pp. 331-337). (International Conference on Artificial Intelligence and Data Engineering, AIDE 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIDE57180.2022.10060313

Sandeep Kini, M. ; Devidas, ; Pai, Smitha N. et al. / Use of Machine Learning and Random OverSampling in Stroke Prediction. International Conference on Artificial Intelligence and Data Engineering, AIDE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 331-337 (International Conference on Artificial Intelligence and Data Engineering, AIDE 2022).

@inproceedings{6acf5e520f0b43b4abced19cde22fff1,

title = "Use of Machine Learning and Random OverSampling in Stroke Prediction",

abstract = "A stroke is a medical condition where blood flow to the brain is interrupted. This is caused because of blood clots. Various factors, like hypertension, diabetes, heart disease, smoking, and body mass index level, result in a stroke. Almost every year, more than 15 million people have a stroke worldwide. Stroke is one of the major reasons of worldwide deaths. Hence prediction of a stroke becomes very crucial. This paper is focused on using various machine learning algorithms, namely, Support Vector Machine(SVM), Polynomial Kernel SVM, Sigmoid Kernel SVM, Gaussian Kernel SVM, Random Forest, and Logistic Regression. The paper also presents a comparison between different algorithms and accuracy scores. Random Forest gave the highest accuracy of 99.5\%. Hence Random Forest is the best-suited model to predict Stroke.",

author = "\{Sandeep Kini\}, M. and Devidas and Pai, \{Smitha N.\} and Sucheta Kolekar and Vasudeva Pai and R. Balasubramani",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Artificial Intelligence and Data Engineering, AIDE 2022 ; Conference date: 22-12-2022 Through 23-12-2022",

year = "2022",

doi = "10.1109/AIDE57180.2022.10060313",

language = "English",

series = "International Conference on Artificial Intelligence and Data Engineering, AIDE 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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address = "United States",

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Sandeep Kini, M, Devidas, , Pai, SN, Kolekar, S, Pai, V & Balasubramani, R 2022, Use of Machine Learning and Random OverSampling in Stroke Prediction. in International Conference on Artificial Intelligence and Data Engineering, AIDE 2022. International Conference on Artificial Intelligence and Data Engineering, AIDE 2022, Institute of Electrical and Electronics Engineers Inc., pp. 331-337, 2022 International Conference on Artificial Intelligence and Data Engineering, AIDE 2022, Karkala, India, 22-12-22. https://doi.org/10.1109/AIDE57180.2022.10060313

Use of Machine Learning and Random OverSampling in Stroke Prediction. / Sandeep Kini, M.; Devidas, ; Pai, Smitha N. et al.
International Conference on Artificial Intelligence and Data Engineering, AIDE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 331-337 (International Conference on Artificial Intelligence and Data Engineering, AIDE 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Use of Machine Learning and Random OverSampling in Stroke Prediction

AU - Sandeep Kini, M.

AU - Devidas,

AU - Pai, Smitha N.

AU - Kolekar, Sucheta

AU - Pai, Vasudeva

AU - Balasubramani, R.

PY - 2022

Y1 - 2022

N2 - A stroke is a medical condition where blood flow to the brain is interrupted. This is caused because of blood clots. Various factors, like hypertension, diabetes, heart disease, smoking, and body mass index level, result in a stroke. Almost every year, more than 15 million people have a stroke worldwide. Stroke is one of the major reasons of worldwide deaths. Hence prediction of a stroke becomes very crucial. This paper is focused on using various machine learning algorithms, namely, Support Vector Machine(SVM), Polynomial Kernel SVM, Sigmoid Kernel SVM, Gaussian Kernel SVM, Random Forest, and Logistic Regression. The paper also presents a comparison between different algorithms and accuracy scores. Random Forest gave the highest accuracy of 99.5%. Hence Random Forest is the best-suited model to predict Stroke.

AB - A stroke is a medical condition where blood flow to the brain is interrupted. This is caused because of blood clots. Various factors, like hypertension, diabetes, heart disease, smoking, and body mass index level, result in a stroke. Almost every year, more than 15 million people have a stroke worldwide. Stroke is one of the major reasons of worldwide deaths. Hence prediction of a stroke becomes very crucial. This paper is focused on using various machine learning algorithms, namely, Support Vector Machine(SVM), Polynomial Kernel SVM, Sigmoid Kernel SVM, Gaussian Kernel SVM, Random Forest, and Logistic Regression. The paper also presents a comparison between different algorithms and accuracy scores. Random Forest gave the highest accuracy of 99.5%. Hence Random Forest is the best-suited model to predict Stroke.

UR - https://www.scopus.com/pages/publications/85150678391

UR - https://www.scopus.com/pages/publications/85150678391#tab=citedBy

U2 - 10.1109/AIDE57180.2022.10060313

DO - 10.1109/AIDE57180.2022.10060313

M3 - Conference contribution

AN - SCOPUS:85150678391

T3 - International Conference on Artificial Intelligence and Data Engineering, AIDE 2022

SP - 331

EP - 337

BT - International Conference on Artificial Intelligence and Data Engineering, AIDE 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 International Conference on Artificial Intelligence and Data Engineering, AIDE 2022

Y2 - 22 December 2022 through 23 December 2022

ER -

Sandeep Kini M, Devidas , Pai SN, Kolekar S, Pai V, Balasubramani R. Use of Machine Learning and Random OverSampling in Stroke Prediction. In International Conference on Artificial Intelligence and Data Engineering, AIDE 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 331-337. (International Conference on Artificial Intelligence and Data Engineering, AIDE 2022). doi: 10.1109/AIDE57180.2022.10060313

Use of Machine Learning and Random OverSampling in Stroke Prediction

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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