AI-Driven Early Detection Models for Chronic Diseases in Saudi Arabia: A Systematic Review and Translational Framework
DOI:
https://doi.org/10.29284/e8swnh23Keywords:
Saudi Arabia; machine learning; early detection; diabetes; cardiovascular disease; cancer; electronic health records; risk prediction; explainable AI; clinical decision supportAbstract
Background : In Saudi Arabia, chronic health issues like diabetes, heart disease, and cancer are on the rise. They lead to avoidable complications and result in higher healthcare expenses. By utilizing clinical data from EHRs and lab results, machine learning can play a significant role in the prevention of potential problems.
Objective : The present review aims to provide research articles from 2020-2025 on how artificial intelligence and machine learning can contribute to the early detection or prediction of risks of developing diabetes, heart disease, and cancer. In addition, it aims to provide a framework that can be applied in the present reporting and evaluation standards.
Methods : The present review was done based on the PRISMA 2020-2025 guidelines, including literature from MEDLINE/PubMed, Scopus, IEEE Xplore, and Google Scholar databases from January 2020 to December 2025.
Results : The evolution of research from small tables of data to bigger populations from EHRs, utilizing feature engineering, and decision tree algorithms was observed. In Saudi Arabia, diabetes research was initially based on modeling disease progression and age of onset, but it now includes modeling disease onset and progression. Cardiovascular disease research was based on stratification and helping in the early diagnosis of the disease.
Conclusion : The potential of artificial intelligence and machine learning in the early detection of diabetes, heart disease, and cancer can be promising. However, it would require better governance and evaluation.
References
1. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71.
2. Liu X, Rivera SC, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. BMJ. 2020;370:m3164. doi:10.1136/bmj.m3164.
3. Cruz Rivera S, Liu X, Chan A-W, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020;26:1351–1363. doi:10.1038/s41591-020-1037-7.
4. Collins GS, Reitsma JB, Altman DG, Moons KGM, et al. TRIPOD-AI: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis using artificial intelligence. BMJ. 2024;384:e078378. doi:10.1136/bmj-2023-078378.
5. Sounderajah V, Ashrafian H, Golub RM, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open. 2021;11:e047709. doi:10.1136/bmjopen-2020-047709.
6. Bossuyt PM, Cohen JF, Reitsma JB, et al. The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence. Nat Med. 2025. doi:10.1038/s41591-025-03953-8.
7. Al-hussein F, Tafakori L, Abdollahian M, Al-Shali K, Al-Hejin A. Predicting Type 2 diabetes onset age using machine learning: A case study in KSA. PLoS ONE. 2025;20(2):e0318484. doi:10.1371/journal.pone.0318484.
8. Alazwari A, Johnstone A, Tafakori L, Abdollahian M, et al. Predicting the development of T1D and identifying its key performance indicators in children: a case-control study in Saudi Arabia. PLoS ONE. 2023;18(3):e0282426. doi:10.1371/journal.pone.0282426.
9. Kopitar L, Kocbek P, Cilar L, Sheikh A, Stiglic G. Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Sci Rep. 2020;10:11981. doi:10.1038/s41598-020-68771-z.
10. Alwakid G, Ul Haq F, Tariq N, Humayun M, Shaheen M, Alsadun M. Optimized machine learning framework for cardiovascular disease diagnosis: a novel ethical perspective. BMC Cardiovasc Disord. 2025;25:123. doi:10.1186/s12872-025-04550-w.
11. Akhtar S, et al. A deep learning based model for diabetic retinopathy grading. Sci Rep. 2025;15:87171. doi:10.1038/s41598-025-87171-9.
12. Mansoori A, et al. Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis. Sci Rep. 2023;13:663. doi:10.1038/s41598-022-27340-2.
13. Almutairi M, et al. Intelligent hybrid modeling for heart disease prediction. Information. 2025;16(10):869. doi:10.3390/info16100869.
14. Tabassum S, et al. A machine learning-based framework for heart disease diagnosis and prediction. Comput Mater Contin. 2025. doi:10.32604/cmc.2025.065423.
15. Levy JJ, O’Malley AJ. Don’t dismiss logistic regression: the case for sensible extraction of interactions in the era of machine learning. BMC Med Res Methodol. 2020;20:171. doi:10.1186/s12874-020-01046-3.
16. Seoni S, Jahmunah V, Salvi M, et al. Application of uncertainty quantification to artificial intelligence in healthcare: a review of last decade (2013–2023). Comput Biol Med. 2023;165:107441. doi:10.1016/j.compbiomed.2023.107441.
17. Zafar I, Anwar S, Ali Y, et al. Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine. Biomed Signal Process Control. 2023;86:105263. doi:10.1016/j.bspc.2023.105263.
18. DECIDE-AI expert group. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022;28:924–933. doi:10.1038/s41591-022-01756-3.
19. Qin Y, Wu J, Xiao W, et al. Machine learning models for data-driven prediction of diabetes by lifestyle type. Int J Environ Res Public Health. 2022;19(22):15027. doi:10.3390/ijerph192215027.
20. Chou C-Y, Hsu D-Y, Chou C-H. Predicting the onset of diabetes with machine learning methods. J Pers Med. 2023;13(3):406. doi:10.3390/jpm13030406.
21. Fazakis N, Kocsis O, Dritsas E, et al. Machine learning tools for long-term type 2 diabetes risk prediction. IEEE Access. 2021;9:103737–103757. doi:10.1109/ACCESS.2021.3098691.
22. Ahamed BS, Arya MS, Nancy VAO. Prediction of Type-2 diabetes mellitus disease using machine learning classifiers and techniques. Front Comput Sci. 2022;4:835242. doi:10.3389/fcomp.2022.835242.
23. Youldash M, et al. Early detection and classification of diabetic retinopathy using deep learning. AI. 2024;5(4):125. doi:10.3390/ai5040125.
24. Chaurasia V, Pal S. Applications of machine learning techniques to predict diagnostic breast cancer. SN Comput Sci. 2020;1:??. doi:10.1007/s42979-020-00296-8.
25. Mouas FE, et al. Machine learning prediction of histological types of breast cancer with hospital data. 2025. doi:10.1016/S2666-5212(25)00079-1.
26. Nahzat S, Yağanoğlu M. Diabetes prediction using machine learning classification algorithms. Avrupa Bilim ve Teknoloji Dergisi. 2021;24:53–59. doi:10.31590/ejosat.899716.
27. Si T, Bagchi J, Miranda PBC. Artificial neural network training using metaheuristics for medical data classification: an experimental study. Expert Syst Appl. 2022;193:116423. doi:10.1016/j.eswa.2021.116423.
28. Peng J, Zhao F, Yang X, et al. Association between dyslipidemia and risk of type 2 diabetes mellitus in middle-aged and older adults: a cohort analysis. BMJ Open. 2021;11:e042821. doi:10.1136/bmjopen-2020-042821.
29. Sarker IH. AI: Real-world applications and research directions. SN Comput Sci. 2021;2:160. doi:10.1007/s42979-021-00592-x.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
