國立虎尾科技大學 |

Integrating Machine Learning with Web Application to Predict Diabetes.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Integrating Machine Learning with Web Application to Predict Diabetes./
作者:	Natarajan, Keerthana.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	36 p.
附註:	Source: Masters Abstracts International, Volume: 83-06.
Contained By:	Masters Abstracts International83-06.
標題:	Information technology. -
電子資源:	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:

559429
Information technology.
Subjects--Index Terms:

Diabetes

Integrating Machine Learning with Web Application to Predict Diabetes.
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