Machine learning technique for detecting diabetes disease

Baydaa Hadi Saoudi; Tabarek Alwan Tuib; Yaqdhan Mahmood Hussein; Yusliza Yusoff; Fahad Taha Al- Dhief; Ali Hashim Abbas

Author(s)

Baydaa Hadi Saoudi Technical Institute-Samawa, Information Technology and Communication (ICT), Al-Furat Al-Awast Technical University,Samawa,Iraq

Tabarek Alwan Tuib Collage of Engineering, Electronics and Communications Engineering, Al Muthanna University,Muthanna,Iraq

Yaqdhan Mahmood Hussein Collage of Engineering, Electronics and Communications Engineering, Al Muthanna University,Muthanna,Iraq

Yusliza Yusoff Applied Industrial Analytics Research Group, Faculty of Computing, Universiti Teknologi Malaysia,Johor Bahru, Johor,Malaysia

Fahad Taha Al- Dhief Faculty of Engineering, School of Electrical Engineering,Universiti Teknologi Malaysia (UTM), Johor Bahru,Malaysia

Ali Hashim Abbas Department of Computer Technical Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University,Al-Muthanna,Iraq

Abstract

Diabetes is considered a critical disease and it has been a growing concern owing to its increased morbidity. Moreover, the average age of people who are affected by diabetes illness has currently declined to the mid-20s. Given the high prevalence, it is necessary to address this problem effectively. Due to the significant prevalence of diabetes illness, it is essential to handle and address this issue appropriately. Currently, machine learning methods are considered a vital area for detecting and diagnosing disease. These methods can learn from data and classify data based on the coordinate subjects. This paper presents a model for detecting diabetes illness based on a machine learning technique. The Support Vector Machine (SVM) algorithm is used for classifying the people who are categorized as patients with diabetes disease from the people who are categorized as non-diabetic. Further, the database is compiled from the Pima Indian Diabetes Dataset (PIDD). The results show that the proposed model achieves 81.8% accuracy. Moreover, the proposed model achieves 84.34% sensitivity and 74.35% specificity.

Publication Details

Page(s) 307-313

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 19, Issue: 5, Year: 2024

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Keywords

Healthcare, SVM, Machine learning, diabetes disease, PIDD database

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