Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms

Diabetes Mellitus (DM) is one of the most prevalent disease in the world today which is associated by having high glucose level in body either due to inadequate production of insulin or the body cell’s not responding towards the produced insulin. Data mining and machine learning techniques can be...

Full description

Main Author: Sirajun Noor, Noor Azmiya
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.23039 /
Published: Universiti Teknologi PETRONAS 2021
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access: http://utpedia.utp.edu.my/23039/1/7_UTP21-2_EE7.pdf
http://utpedia.utp.edu.my/23039/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-utpedia.23039
recordtype eprints
spelling utp-utpedia.230392022-03-11T04:19:55Z http://utpedia.utp.edu.my/23039/ Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms Sirajun Noor, Noor Azmiya TK Electrical engineering. Electronics Nuclear engineering Diabetes Mellitus (DM) is one of the most prevalent disease in the world today which is associated by having high glucose level in body either due to inadequate production of insulin or the body cell’s not responding towards the produced insulin. Data mining and machine learning techniques can be extremely useful in classification of DM considering the need to have a shift from current traditional method which uses sharp needles to draw blood towards a non – invasive method. The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. In this paper, single classifiers such as Support Vector Machine, Naïve Bayes, Bayes Net, Decision Stump, k – Nearest Neighbors, Logistic Regression, Multilayer Perceptron and Decision Tree is experimented. Apart from that, ensemble methods such as bagging, adaptive boosting using AdaBoostM1, hybrid classifier using combinations of Random Forest with various base classifiers and ensemble algorithm which is the Random Forest has also been studied. In this research, it was found that performance of ensemble method using hybrid classifier of Random Forest – Bayes Net model was found as the best DM classification model with an accuracy of 83.91% using the Pima Indian Diabetes Dataset (PIDD) out beating all the other classification algorithms. Whereas for the German Frankfurt dataset, best DM classification model was found using Random Forest algorithm with an accuracy of 98.77%. Universiti Teknologi PETRONAS 2021-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/23039/1/7_UTP21-2_EE7.pdf Sirajun Noor, Noor Azmiya (2021) Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms. Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Sirajun Noor, Noor Azmiya
Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
description Diabetes Mellitus (DM) is one of the most prevalent disease in the world today which is associated by having high glucose level in body either due to inadequate production of insulin or the body cell’s not responding towards the produced insulin. Data mining and machine learning techniques can be extremely useful in classification of DM considering the need to have a shift from current traditional method which uses sharp needles to draw blood towards a non – invasive method. The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. In this paper, single classifiers such as Support Vector Machine, Naïve Bayes, Bayes Net, Decision Stump, k – Nearest Neighbors, Logistic Regression, Multilayer Perceptron and Decision Tree is experimented. Apart from that, ensemble methods such as bagging, adaptive boosting using AdaBoostM1, hybrid classifier using combinations of Random Forest with various base classifiers and ensemble algorithm which is the Random Forest has also been studied. In this research, it was found that performance of ensemble method using hybrid classifier of Random Forest – Bayes Net model was found as the best DM classification model with an accuracy of 83.91% using the Pima Indian Diabetes Dataset (PIDD) out beating all the other classification algorithms. Whereas for the German Frankfurt dataset, best DM classification model was found using Random Forest algorithm with an accuracy of 98.77%.
format Final Year Project
author Sirajun Noor, Noor Azmiya
author_sort Sirajun Noor, Noor Azmiya
title Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
title_short Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
title_full Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
title_fullStr Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
title_full_unstemmed Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
title_sort classification of diabetes mellitus (dm) using machine learning algorithms
publisher Universiti Teknologi PETRONAS
publishDate 2021
url http://utpedia.utp.edu.my/23039/1/7_UTP21-2_EE7.pdf
http://utpedia.utp.edu.my/23039/
_version_ 1741195898797424640
score 11.62408

Similar Items