Machine Learning Based Diabetic Prediction Using Random Forest

Soumyalatha Naveen; Shruti Mishra; Bhavana; P. Pujitha; J. Chandana; Priyanka

doi:10.1109/ViTECoN58111.2023.10157881

Machine Learning Based Diabetic Prediction Using Random Forest

Soumyalatha Naveen
, Shruti Mishra
, Bhavana
, P. Pujitha
, J. Chandana
, Priyanka

School of Computer Engineering

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

5 Citations (Scopus)

Abstract

In recent years, the global impact on diabetics has increased, which is a significant issue. In this case, the patient is obliged to visit a diagnostic centre persistently to get their reports and after consultation investing time and currency on it will be inconvenient. Because of these reasons, outcomes may be severe if unnoticed. An increase in machine learning approaches solves this crucial disadvantage. The objective of this study is to create a method that helps to achieve an early prediction of diabetics with higher precision using random forest algorithm. The degree of precision is higher than other algorithms, with random forest we achieved an accuracy of 85.6% and found to be better algorithm for diabetic prediction comparing with other algorithms such as logistic regression, Naive Bayes, Gradient boosting classifier, KNN and SVM. Random forest yields effective outcomes for predicting diabetics and the result showed that the predictive method can predict the diabetics.

Original language	English
Title of host publication	ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings
Editors	Thanikaiselvan V Thanikaiselvan V, Renuga Devi S, Shankar T, Kalaivani S
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350347982
DOIs	https://doi.org/10.1109/ViTECoN58111.2023.10157881
Publication status	Published - 2023
Event	2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023 - Vellore, India Duration: 05-05-2023 → 06-05-2023

Publication series

Name	ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings

Conference

Conference	2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023
Country/Territory	India
City	Vellore
Period	05-05-23 → 06-05-23

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

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Instrumentation

Access to Document

10.1109/ViTECoN58111.2023.10157881

Cite this

Naveen, S., Mishra, S., Bhavana, Pujitha, P., Chandana, J., & Priyanka (2023). Machine Learning Based Diabetic Prediction Using Random Forest. In T. V. Thanikaiselvan V, R. D. S, S. T, & K. S (Eds.), ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings (ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ViTECoN58111.2023.10157881

Naveen, Soumyalatha ; Mishra, Shruti ; Bhavana et al. / Machine Learning Based Diabetic Prediction Using Random Forest. ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings. editor / Thanikaiselvan V Thanikaiselvan V ; Renuga Devi S ; Shankar T ; Kalaivani S. Institute of Electrical and Electronics Engineers Inc., 2023. (ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings).

@inproceedings{db85bd7df67e483884e493e3460e9fd8,

title = "Machine Learning Based Diabetic Prediction Using Random Forest",

abstract = "In recent years, the global impact on diabetics has increased, which is a significant issue. In this case, the patient is obliged to visit a diagnostic centre persistently to get their reports and after consultation investing time and currency on it will be inconvenient. Because of these reasons, outcomes may be severe if unnoticed. An increase in machine learning approaches solves this crucial disadvantage. The objective of this study is to create a method that helps to achieve an early prediction of diabetics with higher precision using random forest algorithm. The degree of precision is higher than other algorithms, with random forest we achieved an accuracy of 85.6\% and found to be better algorithm for diabetic prediction comparing with other algorithms such as logistic regression, Naive Bayes, Gradient boosting classifier, KNN and SVM. Random forest yields effective outcomes for predicting diabetics and the result showed that the predictive method can predict the diabetics.",

author = "Soumyalatha Naveen and Shruti Mishra and Bhavana and P. Pujitha and J. Chandana and Priyanka",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023 ; Conference date: 05-05-2023 Through 06-05-2023",

year = "2023",

doi = "10.1109/ViTECoN58111.2023.10157881",

language = "English",

series = "ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings",

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

editor = "\{Thanikaiselvan V\}, \{Thanikaiselvan V\} and S, \{Renuga Devi\} and Shankar T and Kalaivani S",

booktitle = "ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings",

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}

Naveen, S, Mishra, S, Bhavana, Pujitha, P, Chandana, J & Priyanka 2023, Machine Learning Based Diabetic Prediction Using Random Forest. in TV Thanikaiselvan V, RD S, S T & K S (eds), ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings. ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023, Vellore, India, 05-05-23. https://doi.org/10.1109/ViTECoN58111.2023.10157881

Machine Learning Based Diabetic Prediction Using Random Forest. / Naveen, Soumyalatha; Mishra, Shruti; Bhavana et al.
ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings. ed. / Thanikaiselvan V Thanikaiselvan V; Renuga Devi S; Shankar T; Kalaivani S. Institute of Electrical and Electronics Engineers Inc., 2023. (ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings).

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

TY - GEN

T1 - Machine Learning Based Diabetic Prediction Using Random Forest

AU - Naveen, Soumyalatha

AU - Mishra, Shruti

AU - Bhavana,

AU - Pujitha, P.

AU - Chandana, J.

AU - Priyanka,

PY - 2023

Y1 - 2023

N2 - In recent years, the global impact on diabetics has increased, which is a significant issue. In this case, the patient is obliged to visit a diagnostic centre persistently to get their reports and after consultation investing time and currency on it will be inconvenient. Because of these reasons, outcomes may be severe if unnoticed. An increase in machine learning approaches solves this crucial disadvantage. The objective of this study is to create a method that helps to achieve an early prediction of diabetics with higher precision using random forest algorithm. The degree of precision is higher than other algorithms, with random forest we achieved an accuracy of 85.6% and found to be better algorithm for diabetic prediction comparing with other algorithms such as logistic regression, Naive Bayes, Gradient boosting classifier, KNN and SVM. Random forest yields effective outcomes for predicting diabetics and the result showed that the predictive method can predict the diabetics.

AB - In recent years, the global impact on diabetics has increased, which is a significant issue. In this case, the patient is obliged to visit a diagnostic centre persistently to get their reports and after consultation investing time and currency on it will be inconvenient. Because of these reasons, outcomes may be severe if unnoticed. An increase in machine learning approaches solves this crucial disadvantage. The objective of this study is to create a method that helps to achieve an early prediction of diabetics with higher precision using random forest algorithm. The degree of precision is higher than other algorithms, with random forest we achieved an accuracy of 85.6% and found to be better algorithm for diabetic prediction comparing with other algorithms such as logistic regression, Naive Bayes, Gradient boosting classifier, KNN and SVM. Random forest yields effective outcomes for predicting diabetics and the result showed that the predictive method can predict the diabetics.

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U2 - 10.1109/ViTECoN58111.2023.10157881

DO - 10.1109/ViTECoN58111.2023.10157881

M3 - Conference contribution

AN - SCOPUS:85165533785

T3 - ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings

BT - ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings

A2 - Thanikaiselvan V, Thanikaiselvan V

A2 - S, Renuga Devi

A2 - T, Shankar

A2 - S, Kalaivani

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

Y2 - 5 May 2023 through 6 May 2023

ER -

Naveen S, Mishra S, Bhavana, Pujitha P, Chandana J, Priyanka. Machine Learning Based Diabetic Prediction Using Random Forest. In Thanikaiselvan V TV, S RD, T S, S K, editors, ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings). doi: 10.1109/ViTECoN58111.2023.10157881

Machine Learning Based Diabetic Prediction Using Random Forest

Abstract

Publication series

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UN SDGs

All Science Journal Classification (ASJC) codes

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