A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.

Bibliografiset tiedot
Päätekijät:	Farabe, Abdullah Al, Sharika, Tarin Sultana, Raonak, Nahian, Ashraf, Ghalib
Muut tekijät:	Chakrabarty, Amitabha
Aineistotyyppi:	Opinnäyte
Kieli:	English
Julkaistu:	Brac University 2021
Aiheet:	Linear Discriminant Analysis (LDA) Logistic regression Random forest Decision tree KNN and CNN Machine learning. Computer algorithms.
Linkit:	http://hdl.handle.net/10361/14745

id	10361-14745
record_format	dspace
spelling	10361-147452022-01-26T10:04:51Z A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk Farabe, Abdullah Al Sharika, Tarin Sultana Raonak, Nahian Ashraf, Ghalib Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Linear Discriminant Analysis (LDA) Logistic regression Random forest Decision tree KNN and CNN Machine learning. Computer algorithms. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 39-41). The application of Arti cial intelligence (AI) has become a valuable part of medical research. These days diabetes is one of the top maladies on the planet. Nowadays it has become a common disease and alarming as people are living in polluted areas and eating unhygienic foods. People with diabetes are probably going to pass on at a more youthful age than individuals who don't have diabetes. We hope this study could be very helpful in medical science to predict the risk score of type II Diabetes Mellitus (DM). Our model consists of four machine learning algorithms which are- K-Nearest Neighbor, Random forest, Decision tree and Logistic Regression. These algorithms have been applied on a dataset containing 15000 type 2 diabetes patients along with eight features that describe the state of patients such as glucose, BMI, age, pregnancy, blood pressure (BP), Diabetes Pedigree Function, Skin thickness and insulin. Moreover, one deep learning algorithm called CNN has been used. All of the ve algorithms have been used on the dataset and the Random forest gives the best accuracy of almost 92.60 percent where other algorithms give less accuracy. Abdullah Al Farabe Tarin Sultana Sharika Nahian Raonak Ghalib Ashraf B. Computer Science 2021-07-06T16:11:00Z 2021-07-06T16:11:00Z 2019 2019-09 Thesis ID 15101081 ID 15301131 ID 15301109 ID 19141023 http://hdl.handle.net/10361/14745 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 41 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Linear Discriminant Analysis (LDA) Logistic regression Random forest Decision tree KNN and CNN Machine learning. Computer algorithms.
spellingShingle	Linear Discriminant Analysis (LDA) Logistic regression Random forest Decision tree KNN and CNN Machine learning. Computer algorithms. Farabe, Abdullah Al Sharika, Tarin Sultana Raonak, Nahian Ashraf, Ghalib A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.
author2	Chakrabarty, Amitabha
author_facet	Chakrabarty, Amitabha Farabe, Abdullah Al Sharika, Tarin Sultana Raonak, Nahian Ashraf, Ghalib
format	Thesis
author	Farabe, Abdullah Al Sharika, Tarin Sultana Raonak, Nahian Ashraf, Ghalib
author_sort	Farabe, Abdullah Al
title	A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
title_short	A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
title_full	A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
title_fullStr	A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
title_full_unstemmed	A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
title_sort	supervised learning approach by machine learning and deep learning algorithms to predict type ii dm risk
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/14745
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A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk

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