國立虎尾科技大學 |

Integrating Machine Learning with Web Application to Predict Diabetes.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Integrating Machine Learning with Web Application to Predict Diabetes./
Author:	Natarajan, Keerthana.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	36 p.
Notes:	Source: Masters Abstracts International, Volume: 83-06.
Contained By:	Masters Abstracts International83-06.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28890322
ISBN:	9798492757076

Integrating Machine Learning with Web Application to Predict Diabetes.
Natarajan, Keerthana.

Integrating Machine Learning with Web Application to Predict Diabetes. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 36 p.

Source: Masters Abstracts International, Volume: 83-06.

Thesis (M.S.)--University of Cincinnati, 2021.

This item must not be sold to any third party vendors.

Diabetes is one of the highest causes of death in the world. Diabetes is caused when the blood glucose level is too high in the body. Gradually, high blood glucose leads to heart disease, stroke, eye, and foot problems. To prevent the dreadful effects among people, early detection is required that would lead to proper medical treatment and change in lifestyle. Therefore, with the rise of machine learning we can predict if a patient has diabetes or not. Furthermore, we will integrate the trained model to a web application that will connect the model to generate predictions in real-time considering factors responsible for diabetes like body mass index (BMI), age, insulin, etc. In this paper, we are using the Pima Indian dataset that is originally from the National Institute of Diabetes, Digestive and Kidney Diseases for diabetes prediction model design using machine learning. The proposed system in this paper is the Soft Voting ensemble classifier. The algorithm with the best accurate result was used in making predictions. This model was deployed to the web using flask (a python framework), it takes inputs from the user to make predictions. This model is implemented using python programming language and flask (a web base framework) hosted in GCP. Soft Voting ensemble classifiers even perform better than other classifiers with an accuracy of 91.55% which is quite promising considering the other classification models in the literature for this problem.

ISBN: 9798492757076Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Diabetes

Integrating Machine Learning with Web Application to Predict Diabetes.
LDR:02645nam a2200373 4500 001 1067286
005 20220823142337.5
008 221020s2021 ||||||||||||||||| ||eng d
020 $a 9798492757076
035 $a (MiAaPQ)AAI28890322
035 $a (MiAaPQ)OhioLINKucin1627663657558303
035 $a AAI28890322
040 $a MiAaPQ $c MiAaPQ
100 1 $a Natarajan, Keerthana. $3 1372821
245 1 0 $a Integrating Machine Learning with Web Application to Predict Diabetes.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 36 p.
500 $a Source: Masters Abstracts International, Volume: 83-06.
502 $a Thesis (M.S.)--University of Cincinnati, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Diabetes is one of the highest causes of death in the world. Diabetes is caused when the blood glucose level is too high in the body. Gradually, high blood glucose leads to heart disease, stroke, eye, and foot problems. To prevent the dreadful effects among people, early detection is required that would lead to proper medical treatment and change in lifestyle. Therefore, with the rise of machine learning we can predict if a patient has diabetes or not. Furthermore, we will integrate the trained model to a web application that will connect the model to generate predictions in real-time considering factors responsible for diabetes like body mass index (BMI), age, insulin, etc. In this paper, we are using the Pima Indian dataset that is originally from the National Institute of Diabetes, Digestive and Kidney Diseases for diabetes prediction model design using machine learning. The proposed system in this paper is the Soft Voting ensemble classifier. The algorithm with the best accurate result was used in making predictions. This model was deployed to the web using flask (a python framework), it takes inputs from the user to make predictions. This model is implemented using python programming language and flask (a web base framework) hosted in GCP. Soft Voting ensemble classifiers even perform better than other classifiers with an accuracy of 91.55% which is quite promising considering the other classification models in the literature for this problem.
590 $a School code: 0045.
650 4 $a Computer science. $3 573171
650 4 $a Information technology. $3 559429
653 $a Diabetes
653 $a Machine learning
653 $a Pima Indian dataset
653 $a Flask python web app
653 $a Google cloud platform
653 $a Soft voting ensemble classifier
690 $a 0984
690 $a 0489
710 2 $a University of Cincinnati. $b Education, Criminal Justice, and Human Services: Information Technology. $3 1372822
773 0 $t Masters Abstracts International $g 83-06.
790 $a 0045
791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28890322