Diabetes Prediction Using Machine Learning Algorithms and Ontology

Authors

  • Hakim El Massari National School of Applied Sciences, Sultan Moulay Slimane University, Lasti Laboratory, Khouribga, Morocco
  • Zineb Sabouri National School of Applied Sciences, Sultan Moulay Slimane University, Lasti Laboratory, Khouribga, Morocco
  • Sajida Mhammedi National School of Applied Sciences, Sultan Moulay Slimane University, Lasti Laboratory, Khouribga, Morocco
  • Noreddine Gherabi National School of Applied Sciences, Sultan Moulay Slimane University, Lasti Laboratory, Khouribga, Morocco https://orcid.org/0000-0002-2020-249X

DOI:

https://doi.org/10.13052/jicts2245-800X.10212

Keywords:

Machine Learning, Ontology, Diabetes, Prediction

Abstract

Diabetes is one of the chronic diseases, which is increasing from year to year. The problems begin when diabetes is not detected at an early phase and diagnosed properly at the appropriate time. Different machine learning techniques, as well as ontology-based ML techniques, have recently played an important role in medical science by developing an automated system that can detect diabetes patients. This paper provides a comparative study and review of the most popular machine learning techniques and ontology-based Machine Learning classification. Various types of classification algorithms were considered namely: SVM, KNN, ANN, Naive Bayes, Logistic regression, and Decision Tree. The results are evaluated based on performance metrics like Recall, Accuracy, Precision, and F-Measure that are derived from the confusion matrix. The experimental results showed that the best accuracy goes for ontology classifiers and SVM.

Downloads

Download data is not yet available.

Author Biographies

Hakim El Massari, National School of Applied Sciences, Sultan Moulay Slimane University, Lasti Laboratory, Khouribga, Morocco

Hakim El Massari received his master degree from Normal Superior School of Abdelmalek Essaadi University, Tétouan, Morocco, in 2014. Currently, he is preparing his Ph.D. in computer science at the National School of Applied Sciences, Sultan Moulay Slimane University, Khouribga, Morocco. His research areas include Machine Learning, Deep Learning, Big Data, Semantic Web, and Ontology. He can be contacted at email: h.elmassari@usms.ma.

Zineb Sabouri, National School of Applied Sciences, Sultan Moulay Slimane University, Lasti Laboratory, Khouribga, Morocco

Zineb Sabouri received her in Computer Engineering degree from the National School of Applied Sciences of Khouribga, Morocco. She worked as a computer engineer in a multinational. Currently, she is a Phd Student in computer science at Sultane Moulay Slimane University. Her area of interest is Machine Learning, Intelligent Systems, Deep Learning, and Big Data.

Sajida Mhammedi, National School of Applied Sciences, Sultan Moulay Slimane University, Lasti Laboratory, Khouribga, Morocco

Sajida Mhammedi received her Ms Degree in Computer Engineering from Faculty of Science and Technologie, Beni Mellal Morocco, She worked as a visiting researcher at the Sultane Moulay Slimane University, Her research interests include Machine Learning, Semantic Web, recommendation systems, Ontology, and Big Data.

Noreddine Gherabi, National School of Applied Sciences, Sultan Moulay Slimane University, Lasti Laboratory, Khouribga, Morocco

Noreddine Gherabi is a professor of computer science with industrial and academic experience. He holds a doctorate degree in computer science. In 2013, he worked as a professor of computer science at Mohamed Ben Abdellah University and since 2015 has worked as a research professor at Sultan Moulay Slimane University, Morocco. Member of the International Association of Engineers (IAENG).

Professor Gherabi having several contributions in information systems namely: big data, semantic web, pattern recognition, intelligent systems … .

He has several papers (book chapters, international journals, and conferences/workshops), and edited books. He has served on executive and technical program committees and as a reviewer of numerous international conferences and journals, he convened and chaired more than 30 conferences and workshops. He is member of the editorial board of several other renowned international journals:

• Co-editor in chief (Editorial Board) in the journal “The International Journal of sports science and engineering for children” (IJSSEC).

• Associate Editor in the journal “International Journal of Engineering Research and Sports Science”.

• Reviewer in several journals/Conferences

• Excellence Award, the best innovation in science and technology 2009

Last books in Springer :

• Intelligent Systems in Big Data, Semantic Web and Machine Learning

• Advances in Information, Communication and Cybersecurity

• Information Technology and Communication Systems

His research areas include Machine Learning, Deep Learning, Big Data, Semantic Web, and Ontology. He can be contacted at email: n.gherabi@usms.ma.

References

Z. Sabouri, Y. Maleh, and N. Gherabi, “Benchmarking Classification Algorithms for Measuring the Performance on Maintainable Applications,” in Advances in Information, Communication and Cybersecurity, Cham, 2022, pp. 173–179. doi: 10.1007/978-3-030-91738-8_17.

H. EL Massari, S. Mhammedi, Z. Sabouri, and N. Gherabi, “Ontology-Based Machine Learning to Predict Diabetes Patients,” in Advances in Information, Communication and Cybersecurity, Cham, 2022, pp. 437–445. doi: 10.1007/978-3-030-91738-8_40.

F. Alaa Khaleel and A. M. Al-Bakry, “Diagnosis of diabetes using machine learning algorithms,” Mater. Today Proc., Jul. 2021, doi: 10.1016/j.matpr.2021.07.196.

J. J. Khanam and S. Y. Foo, “A comparison of machine learning algorithms for diabetes prediction,” ICT Express, vol. 7, no. 4, pp. 432–439, Dec. 2021, doi: 10.1016/j.icte.2021.02.004.

P. Cıhan and H. Coşkun, “Performance Comparison of Machine Learning Models for Diabetes Prediction,” in 2021 29th Signal Processing and Communications Applications Conference (SIU), Jun. 2021, pp. 1–4. doi: 10.1109/SIU53274.2021.9477824.

M. A. Sarwar, N. Kamal, W. Hamid, and M. A. Shah, “Prediction of Diabetes Using Machine Learning Algorithms in Healthcare,” in 2018 24th International Conference on Automation and Computing (ICAC), Sep. 2018, pp. 1–6. doi: 10.23919/IConAC.2018.8748992.

A. Mujumdar and V. Vaidehi, “Diabetes Prediction using Machine Learning Algorithms,” Procedia Comput. Sci., vol. 165, pp. 292–299, Jan. 2019, doi: 10.1016/j.procs.2020.01.047.

M. Rady, K. Moussa, M. Mostafa, A. Elbasry, Z. Ezzat, and W. Medhat, “Diabetes Prediction Using Machine Learning: A Comparative Study,” in 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES), Oct. 2021, pp. 279–282. doi: 10.1109/NILES53778.2021.9600091.

M. U. Emon, M. S. Keya, Md. S. Kaiser, Md. A. islam, T. Tanha, and Md. S. Zulfiker, “Primary Stage of Diabetes Prediction using Machine Learning Approaches,” in 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Mar. 2021, pp. 364–367. doi: 10.1109/ICAIS50930.2021.9395968.

T. Mahboob Alam et al., “A model for early prediction of diabetes,” Inform. Med. Unlocked, vol. 16, p. 100204, Jan. 2019, doi: 10.1016/j.imu.2019.100204.

N. Yuvaraj and K. R. SriPreethaa, “Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster,” Clust. Comput., vol. 22, no. 1, pp. 1–9, Jan. 2019, doi: 10.1007/s10586-017-1532-x.

G. Tripathi and R. Kumar, “Early Prediction of Diabetes Mellitus Using Machine Learning,” in 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Jun. 2020, pp. 1009–1014. doi: 10.1109/ICRITO48877.2020.9197832.

Y. Jian, M. Pasquier, A. Sagahyroon, and F. Aloul, “A Machine Learning Approach to Predicting Diabetes Complications,” Healthcare, vol. 9, no. 12, Art. no. 12, Dec. 2021, doi: 10.3390/healthcare9121712.

S. Barik, S. Mohanty, S. Mohanty, and D. Singh, “Analysis of Prediction Accuracy of Diabetes Using Classifier and Hybrid Machine Learning Techniques,” in Intelligent and Cloud Computing, Singapore, 2021, pp. 399–409. doi: 10.1007/978-981-15-6202-0_41.

K. Pavani, P. Anjaiah, N. V. Krishna Rao, Y. Deepthi, D. Noel, and V. Lokesh, “Diabetes Prediction Using Machine Learning Techniques: A Comparative Analysis,” in Energy Systems, Drives and Automations, Singapore, 2020, pp. 419–428. doi: 10.1007/978-981-15-5089-8_41.

Nejjahi, R., Gherabi, N., Marzouk, A. “Towards Classification of Web Ontologies Using the Horizontal and Vertical Segmentation”, Advances in Intelligent Systems and Computingthis link is disabled, 2018, 640, pp. 70–81.

S. Srivastava, “Weka: A Tool for Data preprocessing, Classification, Ensemble, Clustering and Association Rule Mining,” Int. J. Comput. Appl., vol. 88, no. 10, pp. 26–29, Feb. 2014.

A. Daoui, N. Gherabi, A. Marzouk: A New Approach For Measuring Semantic Similarity Of Ontology Concepts Using Dynamic Programming: Journal of Theoretical and Applied Information Technology 95(17), 4132–4139 (2017).

M. A. Musen, “The protégé project: a look back and a look forward,” AI Matters, vol. 1, no. 4, pp. 4–12, Jun. 2015, doi: 10.1145/2757001.2757003.

Downloads

Published

2022-05-21

How to Cite

Massari, H. E. ., Sabouri, Z. ., Mhammedi, S. ., & Gherabi, N. . (2022). Diabetes Prediction Using Machine Learning Algorithms and Ontology. Journal of ICT Standardization, 10(02), 319–338. https://doi.org/10.13052/jicts2245-800X.10212

Issue

2022: Vol 10 Iss 2

Section

Intelligent Systems for Smart Applications