A Recommendation System for Diabetes Detection and Treatment

Almatrooshi, Fatima; Alhammadi, Sumayya; Salloum, Said A.; Akour, Iman; Shaalan, Khaled

doi:10.1109/ccci49893.2020.9256676

Cited by 10 publications

(2 citation statements)

References 12 publications

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“…Almatrooshi et al [ 17 ] proposed an integration of two systems to create a system that is able to detect diabetes and after that recommend a proper plan or medication to overcome diabetes, they evaluated and tested four different approaches to detection diabetes, the most accurate approach was random forest with an accuracy of 79.2% and F-measure of 0.787.…”

Section: Introductionmentioning

confidence: 99%

Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms

et al. 2021

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Diabetes incidence has been a problem, because according with the World Health Organization and the International Diabetes Federation, the number of people with this disease is increasing very fast all over the world. Diabetic treatment is important to prevent the development of several complications, also lipid profile monitoring is important. For that reason the aim of this work is the implementation of machine learning algorithms that are able to classify cases, that corresponds to patients diagnosed with diabetes that have diabetes treatment, and controls that refers to subjects who do not have diabetes treatment but some of them have diabetes, bases on lipids profile levels. Logistic regression, K-nearest neighbor, decision trees and random forest were implemented, all of them were evaluated with accuracy, sensitivity, specificity and AUC-ROC curve metrics. Artificial neural network obtain an acurracy of 0.685 and an AUC value of 0.750, logistic regression achieve an accuracy of 0.729 and an AUC value of 0.795, K-nearest neighbor gets an accuracy of 0.669 and an AUC value of 0.709, on the other hand, decision tree reached an accuracy pg 0.691 and a AUC value of 0.683, finally random forest achieve an accuracy of 0.704 and an AUC curve of 0.776. The performance of all models was statistically significant, but the best performance model for this problem corresponds to logistic regression.

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Section: Introductionmentioning

confidence: 99%

Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms

et al. 2021

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“…In the current era, the rapid increase in the volume of the data published over the networks increased the need for new techniques to extract the knowledge, analyze, and summarize it. So artificial intelligence comes to resolve these issues by providing many domains that facilitate extraction summarization and analyzing for data [1][2][3][4][5][6][7]. One of these domains is Natural language processing, which specialized in analyzing natural human language [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Qualitative Study in Natural Language Processing: Text Classification

Wahdan

Salloum

Shaalan

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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Recently Natural Language Processing (NLP) has excessive attention due to increased data available online. Although there is huge development in Arabic NLP, but still it is behind English NLP. The aim of this study is to examine the most useful qualitative research design that can be used in the field of Arabic Natural Language Processing in general and in-text classification in specific. Two research designs have been examined in detail; survey and experimental research. Both benefits and drawbacks illustrated for each research design with examples for each method. Thus to have better understanding for some of the research methods. Then deciding which one is best fit to design a qualitative study in this field. Furthermore, this paper suggested framework can be used while researching in the field of NLP. The framework suggested to start with survey or Literature review then follow it with experiment.

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