Computer-aided system for extending the performance of diabetes analysis and prediction

Murad, Saydul Akbar and Zafril Rizal, M Azmi and Zaid Hafiz, Hakami and Prottasha, Nusrat Jahan and Kowsher, Md (2021) Computer-aided system for extending the performance of diabetes analysis and prediction. In: 7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021 , 24-26 Aug. 2021 , Pekan, Malaysia. 465 -470.. ISBN 978-166541407-4

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Abstract

Every year, diabetes causes health difficulties for hundreds of millions of individuals throughout the world. Patients’ medical records may be utilized to quantify symptoms, physical characteristics, and clinical laboratory test data, which may then be utilized to undertake biostatistics analysis to uncover patterns or characteristics that are now undetected. In this work, we have used six machine learning algorithms to give the prediction of diabetes patients and the reason for diabetes are illustrated in percentage using pie charts. The machine learning algorithms used to predict the risks of Type 2 diabetes. User can self-assess their diabetes risk once the model has been trained. Based on the experimental results in AdaBoost Classifier's, the accuracy achieved is almost 98 percent.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Diabetes; AdaBoost Classifier; Random Forest Classifier; K-Nearest Neighbors Classifier; Bernoulli NB; MLP Classifier and Impact Learning; Cloud Computing
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Faculty/Division:	Institute of Postgraduate Studies Faculty of Computing
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	04 Jul 2022 01:58
Last Modified:	04 Jul 2022 01:58
URI:	http://umpir.ump.edu.my/id/eprint/34577
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