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8 June 2022 Classification of diabetes mellitus disease using machine learning
Mahmoud Adnan Mohamed, Ali Bou Nassif, Mohammad AlShabi
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Proceedings Volume 12123, Smart Biomedical and Physiological Sensor Technology XIX; 121230C (2022) https://doi.org/10.1117/12.2632632
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States
Abstract
Diabetes mellitus, also known as just diabetes, is a medical condition marked by a high blood sugar level over long period of time. If diabetes left untreated it can result in damaging the nerves, kidney diseases, foot ulcers, damaging eyes, and in worst cases diabetes leads to death. The purpose of this study is to examine and compare numerous machine learning algorithms in order to determine the best forecasting algorithm based on various metrics such as accuracy, precision, recall, F-measure, kappa, sensitivity, and specificity. Four machine learning algorithms will be investigated in this paper such as Random Forest (RF), Support Victor Machine (SVM), K nearest neighbor (k-NN), and Classification and Regression Trees (CART). Algorithms are used in a comprehensive investigation on diabetes dataset. The obtained findings suggest that, when compared to other algorithms, RF provides more accurate predictions.
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Mahmoud Adnan Mohamed, Ali Bou Nassif, and Mohammad AlShabi "Classification of diabetes mellitus disease using machine learning", Proc. SPIE 12123, Smart Biomedical and Physiological Sensor Technology XIX, 121230C (8 June 2022); https://doi.org/10.1117/12.2632632
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KEYWORDS
Machine learning
Data modeling
Classification systems
Feature selection
Mathematical modeling
Glucose
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