Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy

Tao Hai; Jincheng Zhou; Timothy A. Olatunji; Oluwakemi A. Ajoboh; Lee Chen; Celestine Iwendi; Nkechi Omeoga; Anurag Sinha

doi:10.1007/978-3-031-37164-6_40

Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy

Tao Hai
, Jincheng Zhou
, Timothy A. Olatunji
, Oluwakemi A. Ajoboh
, Lee Chen
, Celestine Iwendi
, Nkechi Omeoga
, Anurag Sinha

Qiannan Normal College for Nationalities
Guizhou University
Nanchang Institute of Science and Technology
Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province
University of Bolton
Manchester Metropolitan University
Indira Gandhi National Open University

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

Abstract

To improve the accuracy in the detection of diabetes-type diagnosis using Random Forest Algorithm and Support Vector Method. A dataset on diabetes was collected on kaggle.com. Support vector method analysis was performed on the data using kernel as linear, poly, and rbf with random state = 42 and test size = 0.2. The Random Forest Algorithm with n_estimators = 100 has the same accuracy as the support vector method with the kernel as rbf. When it comes to type diagnosis for a patient having diabetes, the Random Forest Algorithm and Support Vector Method with kernel rbf can be used, unlike another kernel of the Support Vector Method. The dataset was also visualized using power.

Original language	English
Title of host publication	Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions
Editors	Celestine Iwendi, Zakaria Boulouard, Natalia Kryvinska
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	549-555
Number of pages	7
ISBN (Print)	9783031371639
DOIs	https://doi.org/10.1007/978-3-031-37164-6_40
State	Published - 2023
Externally published	Yes
Event	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 - Bolton, United Kingdom Duration: 24 Feb 2023 → 25 Feb 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	735 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023
Country/Territory	United Kingdom
City	Bolton
Period	24/02/23 → 25/02/23

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Diabetes diagnosis
Machine learning
Power BI
Random forest algorithm
Regression
Support vector method

Access to Document

10.1007/978-3-031-37164-6_40

Cite this

Hai, T., Zhou, J., Olatunji, T. A., Ajoboh, O. A., Chen, L., Iwendi, C., Omeoga, N., & Sinha, A. (2023). Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy. In C. Iwendi, Z. Boulouard, & N. Kryvinska (Eds.), Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions (pp. 549-555). (Lecture Notes in Networks and Systems; Vol. 735 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37164-6_40

Hai, Tao ; Zhou, Jincheng ; Olatunji, Timothy A. et al. / Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy. Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. editor / Celestine Iwendi ; Zakaria Boulouard ; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. pp. 549-555 (Lecture Notes in Networks and Systems).

@inproceedings{0a6a6f22b31f4cc290c6246f7f124635,

title = "Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy",

abstract = "To improve the accuracy in the detection of diabetes-type diagnosis using Random Forest Algorithm and Support Vector Method. A dataset on diabetes was collected on kaggle.com. Support vector method analysis was performed on the data using kernel as linear, poly, and rbf with random state = 42 and test size = 0.2. The Random Forest Algorithm with n\_estimators = 100 has the same accuracy as the support vector method with the kernel as rbf. When it comes to type diagnosis for a patient having diabetes, the Random Forest Algorithm and Support Vector Method with kernel rbf can be used, unlike another kernel of the Support Vector Method. The dataset was also visualized using power.",

keywords = "Diabetes diagnosis, Machine learning, Power BI, Random forest algorithm, Regression, Support vector method",

author = "Tao Hai and Jincheng Zhou and Olatunji, \{Timothy A.\} and Ajoboh, \{Oluwakemi A.\} and Lee Chen and Celestine Iwendi and Nkechi Omeoga and Anurag Sinha",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 ; Conference date: 24-02-2023 Through 25-02-2023",

year = "2023",

doi = "10.1007/978-3-031-37164-6\_40",

language = "English",

isbn = "9783031371639",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "549--555",

editor = "Celestine Iwendi and Zakaria Boulouard and Natalia Kryvinska",

booktitle = "Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions",

address = "Germany",

}

Hai, T, Zhou, J, Olatunji, TA, Ajoboh, OA, Chen, L, Iwendi, C, Omeoga, N & Sinha, A 2023, Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy. in C Iwendi, Z Boulouard & N Kryvinska (eds), Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. Lecture Notes in Networks and Systems, vol. 735 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 549-555, International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023, Bolton, United Kingdom, 24/02/23. https://doi.org/10.1007/978-3-031-37164-6_40

Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy. / Hai, Tao; Zhou, Jincheng; Olatunji, Timothy A. et al.
Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. ed. / Celestine Iwendi; Zakaria Boulouard; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. p. 549-555 (Lecture Notes in Networks and Systems; Vol. 735 LNNS).

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

TY - GEN

T1 - Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy

AU - Hai, Tao

AU - Zhou, Jincheng

AU - Olatunji, Timothy A.

AU - Ajoboh, Oluwakemi A.

AU - Chen, Lee

AU - Iwendi, Celestine

AU - Omeoga, Nkechi

AU - Sinha, Anurag

PY - 2023

Y1 - 2023

N2 - To improve the accuracy in the detection of diabetes-type diagnosis using Random Forest Algorithm and Support Vector Method. A dataset on diabetes was collected on kaggle.com. Support vector method analysis was performed on the data using kernel as linear, poly, and rbf with random state = 42 and test size = 0.2. The Random Forest Algorithm with n_estimators = 100 has the same accuracy as the support vector method with the kernel as rbf. When it comes to type diagnosis for a patient having diabetes, the Random Forest Algorithm and Support Vector Method with kernel rbf can be used, unlike another kernel of the Support Vector Method. The dataset was also visualized using power.

AB - To improve the accuracy in the detection of diabetes-type diagnosis using Random Forest Algorithm and Support Vector Method. A dataset on diabetes was collected on kaggle.com. Support vector method analysis was performed on the data using kernel as linear, poly, and rbf with random state = 42 and test size = 0.2. The Random Forest Algorithm with n_estimators = 100 has the same accuracy as the support vector method with the kernel as rbf. When it comes to type diagnosis for a patient having diabetes, the Random Forest Algorithm and Support Vector Method with kernel rbf can be used, unlike another kernel of the Support Vector Method. The dataset was also visualized using power.

KW - Diabetes diagnosis

KW - Machine learning

KW - Power BI

KW - Random forest algorithm

KW - Regression

KW - Support vector method

UR - https://www.scopus.com/pages/publications/85174530940

U2 - 10.1007/978-3-031-37164-6_40

DO - 10.1007/978-3-031-37164-6_40

M3 - Conference contribution

AN - SCOPUS:85174530940

SN - 9783031371639

T3 - Lecture Notes in Networks and Systems

SP - 549

EP - 555

BT - Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions

A2 - Iwendi, Celestine

A2 - Boulouard, Zakaria

A2 - Kryvinska, Natalia

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

Y2 - 24 February 2023 through 25 February 2023

ER -

Hai T, Zhou J, Olatunji TA, Ajoboh OA, Chen L, Iwendi C et al. Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy. In Iwendi C, Boulouard Z, Kryvinska N, editors, Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. Springer Science and Business Media Deutschland GmbH. 2023. p. 549-555. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-37164-6_40

Diagnosis of Diabetes Type Using Random Forest Algorithm and SVM for Improving Accuracy

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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