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Machine Learning based Model for the Prediction of Fasting Blood Sugar Level towards Cardiovascular Disease Control for the Enhancement of Public Health

by Anietie Ekong, Edward Udo, Otuekong Ekong, Savior Inyang
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 52
Year of Publication: 2023
Authors: Anietie Ekong, Edward Udo, Otuekong Ekong, Savior Inyang
10.5120/ijca2023922622

Anietie Ekong, Edward Udo, Otuekong Ekong, Savior Inyang . Machine Learning based Model for the Prediction of Fasting Blood Sugar Level towards Cardiovascular Disease Control for the Enhancement of Public Health. International Journal of Computer Applications. 184, 52 ( Mar 2023), 5-12. DOI=10.5120/ijca2023922622

@article{ 10.5120/ijca2023922622,
author = { Anietie Ekong, Edward Udo, Otuekong Ekong, Savior Inyang },
title = { Machine Learning based Model for the Prediction of Fasting Blood Sugar Level towards Cardiovascular Disease Control for the Enhancement of Public Health },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2023 },
volume = { 184 },
number = { 52 },
month = { Mar },
year = { 2023 },
issn = { 0975-8887 },
pages = { 5-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number52/32657-2023922622/ },
doi = { 10.5120/ijca2023922622 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:24:39.515291+05:30
%A Anietie Ekong
%A Edward Udo
%A Otuekong Ekong
%A Savior Inyang
%T Machine Learning based Model for the Prediction of Fasting Blood Sugar Level towards Cardiovascular Disease Control for the Enhancement of Public Health
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 52
%P 5-12
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fasting Blood Sugar (FBS) levels reveal important information regarding a person's blood sugar management. There is a strong relationship between a person’s FBS level and cardiovascular disease (CVD) because uncontrolled long-term high FBS level can lead to CVD. Devising a means of predicting Fasting blood Sugar level of a patient will go a long way in proper management of diabetes and in turn help in cardiovascular disease control. Predicting the level of FBS for purposes of controlling CVD is the aim of this research. An all-inclusive review was first carried out on Fasting Blood Sugar, Blood Glucose Test, Diabetes, Cardiovascular disease and Machine Learning. Secondly, General Logistic Model (GLM) was adopted for the prediction of Fasting Blood Sugar levels based on the metrics used. Performance analysis results show effective prediction using the Confusion Matrix and AUC-ROC which gave 70% accuracy on the dataset used. Thirdly, the logistic regression model was deployed to Application Programming Interface (API) where each medical practitioner can adopt and used for predicting patient’s blood sugar level based on the metrics provided.

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Index Terms

Computer Science
Information Sciences

Keywords

Bio-inspired computing Computer aided diagnosis blood sugar Cardiovascular disease Machine Learning Logistic Regression public health.

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