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2025 Vol.16, Issue 4 Preview Page

General Article

AI-Driven approaches for sustainable urban development: A PRISMA systematic review of machine and deep learning applications in occupant health and facility management
Abdullah Haidari12
,
Nader Bahman3*
,
Nesar Ahmad Qauomi4
,
Muktha Eti5
,
Murari Devakannan Kamalesh6
,
Pooja Sharma78
,
Ankit Dilipkumar Oza9
1PhD Student, Ural Federal University named after the first President of Russia B N Yeltsin, Russia
2Research Scholar, N.N. Krasovskii Institute of Mathematic and Mechanics, Yekaterinburg, Russia
3Research Scholar, Ural Federal University named after the first President of Russia B N Yeltsin, Russia
4Research Scholar, National University of Science and Technology MISIS (NUST MISIS), Public university in Moscow, Russia
5Assistant Professor, Department of Electrical and Electronics Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India
6Assistant Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
7Adjunct Faculty, Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India
8Adjunct Faculty, Department of Computer Science, Technical Engineering College, The Islamic University, Najaf, Iraq
9Adjunct Faculty, University Centre for Research and Development, Chandigarh University, Mohali, Punjab, 140413, India
*Corresponding Author
31 December 2025. pp. 461-476
PDF XML Citation
Abstract

Sustainable urban development relies on resilient health systems, healthy occupants, and efficient facility management. Machine Learning (ML) and Deep Learning (DL) provide scalable decision- support tools that can strengthen these dimensions by transforming diverse health and environmental data into actionable insights. Using the PRISMA framework, this systematic review synthesizes ML/DL applications relevant to sustainable cities, with a focus on occupant health and healthcare workforce planning—two domains that directly influence sustainable building operations and urban resilience. We examine methods (tree-based models, CNN/RNN/LSTM, transformers) and data sources (EHRs, IoT and ambient sensors, wearables, environmental exposures, mobility, and administrative records) across tasks such as disease surveillance, risk stratification, bed-capacity forecasting, care-pathway optimization, and workforce scheduling. Reported deployments improve predictive accuracy, reduce wait times, optimize space and resource utilization, and enhance care coordination—contributing to healthier occupants and more sustainable healthcare facilities. However, challenges persist, including fragmented datasets, bias and fairness concerns, privacy governance, and limited reproducibility across urban contexts. To address these, we outline a practical workflow for operationalizing AI in sustainable healthcare and building environments: standards-based data integration (e.g., FHIR), transparent validation, MLOps pipelines, human-in-the-loop decision-making, and fairness auditing. By linking predictive modeling with occupant health monitoring and facility management strategies, this review provides a roadmap for integrating AI into healthcare-enabled sustainable building systems. Ultimately, leveraging ML/DL within urban healthcare infrastructure enhances resilience, efficiency, and well-being—key drivers for sustainable urban development and healthy building practices.

Keywords
ML
DL
support vector machine (SVM)
clinical disease prediction
sustainable urban development
healthy and sustainable buildings
healthcare facility management
human resource optimization
References
1

R. Setyawati, A. Astuti, T.P. Utami, S. Adiwijaya, and D.M. Hasyim, The importance of early detection in disease management. Journal of World Future Medicine, Health and Nursing. 2(1) (2024), pp. 51-63.

2

J.V. Singh, Performance, Slack, and Risk Taking in Organizational Decision-Making. The Academy of Management Journal. 29(3) (1986), pp. 562-585.

10.2307/256224
3

D.W. Young and E. Ballarin, Strategic decision- making in healthcare organizations: it is time to get serious. Int J Health Plann Manage. 21(3) (2006), pp. 173-191.

10.1002/hpm.844
4

R. French, C. Rayner, G. Rees, S. Rumbles, J. Schermerhorn, J. Hunt, and R. Osborn, Organizational Behaviour. 2nd ed. 2011, New York: John Wiley & Sons.

5

World Health Organization (WHO), Recommendations on digital interventions for health system strengthening, WHO Guideline, World Health Organization, 2020-10, 2019.

6

D.S.W. Ting, C.Y.L. Cheung, G. Lim, G.S.W. Tan, N.D. Quang, A. Gan, H. Hamzah, R. Garcia- Franco, I.Y.S. Yeo, S.Y. Lee, E.Y.M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N.C. Tan, E.A. Finkelstein, E.L. Lamoureux, I.Y. Wong, N.M. Bressler, S. Sivaprasad, R. Varma, J.B. Jonas, M.G. He, C.Y. Cheng, G.C.M. Cheung, T. Aung, W. Hsu, M.L. Lee, and T.Y. Wong, Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. J Am Med Assoc. 318(22) (2017), 2211.

10.1001/jama.2017.1815229234807PMC5820739
7

S. Asif, X. Zheng, and Y. Zhu, An optimized fusion of deep learning models for kidney stone detection from CT images. Journal of King Saud University-Computer and Information Sciences. 36(7) (2024), 102130.

10.1016/j.jksuci.2024.102130
8

J. Li, Z. Guan, J. Wang, C.Y. Cheung, Y. Zheng, L.L. Lim, et al., Integrated image-based deep learning and language models for primary diabetes care. Nature Medicine. (2024), pp. 1-11.

9

E.H. Houssein, R.E. Mohamed, and A.A. Ali, Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques. Scientific Reports. 13(1) (2023), 7173.

10.1038/s41598-023-34294-637138014PMC10156668
10

M. Groh, O. Badri, R. Daneshjou, A. Koochek, C. Harris, L.R. Soenksen, P.M. Doraiswamy, and R. Picard, Deep learning-aided decision support for diagnosis of skin disease across skin tones. Nature Medicine. 30(2) (2024), pp. 573-583.

10.1038/s41591-023-02728-338317019PMC10878981
11

S.K.B. Sangeetha, S.K. Mathivanan, P. Karthikeyan, H. Rajadurai, B.D. Shivahare, S. Mallik, and H. Qin, An enhanced multimodal fusion deep learning neural network for lung cancer classification. Systems and Soft Computing. 6 (2024), 200068.

10.1016/j.sasc.2023.200068
12

A. Abraham, N.L. Kavoussi, W. Sui, C. Bejan, J.A. Capra, and R. Hsi, Machine learning prediction of kidney stone composition using electronic health record-derived features. J Endourol. 36(2) (2022), pp. 243-250.

10.1089/end.2021.021134314237PMC8861926
13

C.Y. Ma, Y.M. Luo, T.Y. Zhang, Y.D. Hao, X.Q. Xie, X.W. Liu, X.L. Ren, X.L. He, Y.M. Han, K.J. Deng, D. Yan, H. Yang, H. Tang, and H. Lin, Predicting coronary heart disease in Chinese diabetics using machine learning. Computers in Biology and Medicine. 169 (2024), 107952.

10.1016/j.compbiomed.2024.107952
14

T. Karras, T. Aila, S. Laine, and J. Lehtinen, Progressive growing of GANs for improved quality, stability, and variation. arXiv:1710.10196. (2017).

15

Y. Zhang, Z. Wang, Z. Zhang, J. Liu, Y. Feng, L. Wee, A. Dekker, Q. Chen, and A. Traverso, GAN- based one-dimensional medical data augmentation. Soft Compute. 27(15), pp. 10481-10491.

10.1007/s00500-023-08345-z
16

X. Liu, H. Liu, G. Yang, Z. Jiang, S. Cui, Z. Zhang, H. Wang, L. Tao, Y. Sun, Z. Song, T. Hong, J. Yang, T. Gao, J. Zhang, X. Li, J. Zhang, Y. Sang, Z. Yang, K. Xue, S. Wu, P. Zhang, J. Yang, C. Song, and G. Wang, A generalist medical language model for disease diagnosis assistance. Nature Medicine. 31 (2025), pp. 932-942.

10.1038/s41591-024-03416-6
17

M. Safari, A. Fatemi, and L. Archambault, MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network. BMC Medical Imaging. 23(1) (2023), 203.

10.1186/s12880-023-01160-w38062431PMC10704723
18

A. Dhavan, A.A. Kalse, V.V. Bidnur, and S.N. Gambhire, Utilizing Machine Learning for Predictive Analysis of Employee Turnover and Retention Strategies. Anvesak. (2024), 76.

19

I. Abdurrab, T. Mahmood, S. Sheikh, S. Aijaz, M. Kashif, A. Memon, I. Ali, G. Peerwani, A. Pathan, A.B. Alkhodre, and M.S. Siddiqui, Predicting the length of stay of cardiac patients based on pre- operative variables—bayesian models vs. machine learning models. Healthcare. 12(2) (2024), 249.

10.3390/healthcare1202024938255136PMC10815919
20

M.U. Rehman, A. Shafique, S.S. Jamal, Y. Gheraibia, and A.B. Usman, Voice disorder detection using machine learning algorithms: An application in speech and language pathology. Engineering Applications of Artificial Intelligence. 133 (2024), 108047.

10.1016/j.engappai.2024.108047
21

R. Sharma, A. Jain, and M. Manwal, Enhancing Human Resource Management through Deep Learning: A Predictive Analytics Approach to Employee Retention Success. In 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE. (2024), pp. 1-4.

10.1109/ICITEICS61368.2024.10625175
22

T.A. Daghistani, R. Elshawi, S. Sakr, A.M. Ahmed, A. Al-Thwayee, and M.H. Al-Mallah, Predictors of in-hospital length of stay among cardiac patients: a machine learning approach. International journal of cardiology. 288 (2019), pp. 140-147.

10.1016/j.ijcard.2019.01.046
23

M.J. Page, J.E. McKenzie, P.M. Bossuyt, I. Boutron, T.C. Hoffmann, C.D. Mulrow, et al., The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. (2021), 372.

24

T.A. Lone, A. Rashid, S. Gupta, S.K. Gupta, D.S. Rao, M. Najim, A. Srivastava, A. Kumar, L.S. Umrao, and A. Singhal, Securing communication by attribute-based authentication in HetNet used for medical applications. EURASIP Journal on Wireless Communications and Networking. 146 (2020), pp. 1-21.

10.1186/s13638-020-01759-5
25

S.K. Gupta, R. Sharma, and R.K. Saket, Effect of variation in active route timeout and delete period constant on the performance of AODV protocol. International Journal of Mobile Communications. 12(2) (2014), pp. 177-191.

10.1504/IJMC.2014.059737
26

R. Priyadharshini, V. Sathiyamoorthi, S.K. Gupta, and M. Najim, Performance comparison of various learning algorithms for prediction of CVD risk among OSA patients. GMSARN International Journal. 20(1) (2026), pp. 9-15.

27

S. Mohi Ul Din, S. Gupta, and S.K. Gupta, Health hazard minimization using collaborative multi- UAVs for coverage extension in urban scenario. GMSARN International Journal. 19(4) (2025), pp. 598-608.

28

S. Mohi Ul Din and S.K. Gupta, A novel energy transmission method in WSN for difficult physical situation. GMSARN International Journal. 19(3) (2025), pp. 442-447.

29

I. Sharma and S.K. Gupta, IRS-based drone communication systems for emergency situations. GMSARN International Journal. 19(3) (2025), pp. 485-491.

30

S.K. Gupta, S. Mohi Ul Din, K. Upreti, S. Mahajan, and S.S. Date, Enhancing CNN weights for improved routing in UAV networks for catastrophe relief with MSBO algorithm. Journal of Mobile Multimedia. 20(5) (2024), pp. 1117-1152.

10.13052/jmm1550-4646.2056
31

V. Sharma, Nillmani, S.K. Gupta, and K.K. Shukla, Deep learning models for tuberculosis detection and infected region visualization in chest X-ray images. Intelligent Medicine. 4(2) (2024), pp. 104-113.

10.1016/j.imed.2023.06.001
32

A. Khan, S. Gupta, and S.K. Gupta, UAV-enabled disaster management: Applications, open issues, and challenges. GMSARN International Journal. 18(1) (2024), pp. 44-53.

33

A. Gupta and S.K. Gupta, A study on secured unmanned aerial vehicle-based fog computing networks. SAE International Journal of Connected and Automated Vehicles. 7(2) (2024), pp. 1-11.

10.4271/12-07-02-0011
34

S. Kumar, S. Kumar, M.K. Chaube, S.K. Gupta, and R.K. Saket, Role of mathematical modelling and learning techniques for privacy preservation. GMSARN International Journal. 17(1) (2023), pp. 96-110.

35

P. Asha, V. Hemamalini, N. Swapna, and K.L.S. Soujanya, Human Emotion Recognition Based on Machine Learning Algorithms with low Resource Environment. ACM Transactions on Asian and Low-Resource Language Information Processing. (2024).

10.1145/3640340
36

V.K. Sharma and S.K. Gupta, High-Performance Automation Methods for Computational Intelligent Systems: Challenges, Opportunities, and Applications. 2025, CRC Press, Taylor and Francis Group, 1st Edition, pp. 1-472. ISBN: 9781003559917.

10.1201/9781003559917
37

V.K. Sharma and S.K. Gupta, High-Performance Automation Methods for Computational Intelligent Systems: Design and Enabling Technologies. 2025, CRC Press, Taylor and Francis Group, 1st Edition, pp. 1-472. ISBN: 9781003643609.

10.1201/9781003643609
38

P. Singh, S. Kumar, S.K. Gupta, A.K. Rai, and A. Saif, Wireless Ad-hoc and Sensor Networks: Architecture, Protocols, and Applications. 2024, Routledge, CRC Press, Taylor and Francis Group, 1st Edition, pp. 1-412.

10.1201/9781003528982
39

S.K. Gupta, M. Kumar, A. Nayyar, and S. Mahajan, Unmanned Aircraft Systems. 2025, Scrivener Publishing, Wiley, 1st Edition. ISBN-13: 978-13 94230617.

40

O. Kaiwartya, K. Kaushik, S.K. Gupta, A. Mishra, and M. Kumar, Security and Privacy in Cyberspace. 2022, Springer Nature, 1st Edition, pp. 1-226.

10.1007/978-981-19-1960-2
41

S.S. Kumar, S.T. Ahmed, A.S. Fathima, S.K. Mathivanan, P. Jayagopal, A. Saif, S.K. Gupta, and G. Sinha, iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique. Multimedia Tools and Applications. 83 (2024), pp. 86359-86381.

10.1007/s11042-024-19545-6
42

P. Cihan, The machine learning approach for predicting the number of intensive care, intubated patients and death: The COVID-19 pandemic in Turkey. Sigma Journal of Engineering and Natural Sciences. 40(1) (2022), pp. 85-94.

10.14744/sigma.2022.00007
43

B.S. Price, M. Khodaverdi, A. Halasz, B. Hendricks, W. Kimble, G.S. Smith, and S.L. Hodder, Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: a machine learning enhanced approach. Plos one. 16(11) (2021), e0259538.

10.1371/journal.pone.025953834731188PMC8565789
44

M. Shanbehzadeh, A. Yazdani, M. Shafiee, and H. Kazemi-Arpanahim, Predictive modeling for COVID-19 readmission risk using machine learning algorithms. BMC Med Inform Decis Mak. 22 (2022), 139.

10.1186/s12911-022-01880-z35596167PMC9122247
45

Z.A. Varzaneh, A. Orooji, L. Erfannia, and M. Shanbehzadeh, A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method. Informatics in medicine unlocked. 28 (2022), 100825.

10.1016/j.imu.2021.10082534977330PMC8712462
46

N. Kolluri, Y. Liu, and D. Murthy, COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic. JMIR Infodemiology. 2(2) (2022), e38756.

10.2196/3875637113446PMC9987189
47

A. Yazdani, M. Zahmatkeshan, R. Ravangard, R. Sharifian, and M. Shirdeli, Supervised machine learning approach to COVID-19 detection based on clinical data. Medical Journal of the Islamic Republic of Iran. 36 (2022).

10.47176/mjiri.36.11036447543PMC9700415
48

A. Abugabah and F. Shahid, Intelligent Health Care and Diseases Management System: Multi- Day-Ahead Predictions of COVID-19. Mathematics. 11(4) (2023), 1051.

10.3390/math11041051
49

A. Yazdani, S.K. Bigdeli, and M. Zahmatkeshan, Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran. Health Science Reports. 6(4) (2023), e1212.

10.1002/hsr2.121237064314PMC10099201
50

D. Patel, P. Timsina, L. Gorenstein, B. Glicksberg, G. Raut, S. Cheetirala, F. Santana, J. Tamegue, A. Kia, E. zimlichman, M.A. Levin, R. Freeman, and E. Klang, Comparative Analysis of a Large Language Model and Machine Learning Method for Prediction of Hospitalization from Nurse Triage Notes: Implications for Machine Learning-based Resource Management. medRxiv. (2023), 2023-08.

10.1101/2023.08.07.23293699
51

A.F.A. Hamdan and A.A. Bakar, Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital. The Malaysian Journal of Medical Sciences: MJMS. 30(5) (2023), 169.

10.21315/mjms2023.30.5.1437928795PMC10624443
52

H. Mattie, P. Reidy, P. Bachtiger, E. Lindemer, M. Jouni, and T. Panch, A Framework for Predicting Impactability of Healthcare Interventions Using Machine Learning Methods, Administrative Claims, Sociodemographic and App Generated Data. (2019). arXiv preprint arXiv:1905.00751.

10.1089/pop.2019.0132
53

L. Aufegger, C. Bicknell, E. Soane, H. Ashrafian, and A. Darzi, Understanding health management and safety decisions using signal processing and machine learning. BMC Medical Research Methodology. 19 (2019), pp. 1-12.

10.1186/s12874-019-0756-231196000PMC6567495
54

S. Triberti, I. Durosini, and G. Pravettoni, A “third wheel” effect in health decision making involving artificial entities: A psychological perspective. Frontiers in Public Health. 8(2020), 117.

10.3389/fpubh.2020.0011732411641PMC7199477
55

S. Sandhu, A.L. Lin, N. Brajer, J. Sperling, W. Ratliff, A.D. Bedoya, S. Balu, C. O’Brien, and M.P. Sendak, Integrating a machine learning system into clinical workflows: qualitative study. Journal of Medical Internet Research. 22(11) (2020), e22421.

10.2196/2242133211015PMC7714645
56

I. Sharma, and S.K. Gupta, Channel Tracking in IRS-based UAV Communication Systems using Federated Learning. Journal of Electrical Engineering. 74(6) (2023), pp. 521-531.

10.2478/jee-2023-0060
57

C. Chunka, S. Banerjee, and S.K. Gupta, A secure communication using multifactor authentication and key agreement techniques in internet of medical things for COVID-19 patients. Concurrency and Computation: Practice and Experience. 35(7) (2023), pp. 1-22.

10.1002/cpe.7602
58

O. Yakusheva, J.T. Bang, R.G. Hughes, K.L. Bobay, L. Costa, and M.E. Weiss, Nonlinear association of nurse staffing and readmissions uncovered in machine learning analysis. Health services research, 57(2) (2022), pp. 311-321.

10.1111/1475-6773.1369534195989PMC8928027
59

A. Mishra and S.K. Gupta, Intelligent classification of coal seams using spontaneous combustion susceptibility in IoT paradigm. International Journal of Coal Preparation and Utilization. 44(7) (2023), pp. 1-23.

10.1080/19392699.2023.2217747
60

V. Pathak, K. Singh, R.R. Chandan, S.K. Gupta, M. Kumar, S. Bhushan, and S. Jayaprakash, Efficient compression sensing mechanism-based WBAN system. Security and Communication Networks. (2023), pp. 1-12.

10.1155/2023/8468745
61

S. Kumar, M.K. Chaube, S.H. Alsamhi, S.K. Gupta, M. Guizani, R. Gravina, and G. Fortino, A novel multimodal fusion framework for early diagnosis and accurate classification of COVID- 19 patients using X-ray images and speech signal processing techniques. Computer Methods and Programs in Biomedicine. 226 (2022), 107109.

10.1016/j.cmpb.2022.10710936174422PMC9465496
62

S. Kumar, R. Nagar, S. Bhatnagar, R. Vaddi, S.K. Gupta, M. Rashid, A.K. Bashir, and T. Alkhalifah, Chest X-ray and cough sample-based deep learning framework for accurate diagnosis of COVID-19. Computers and Electrical Engineering. 103 (2022), 108391.

10.1016/j.compeleceng.2022.10839136119394PMC9472671
63

L. Liu, Y. Ni, N. Zhang, and J. Nick Pratap, Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children’s surgery. International Journal of Medical Informatics. 129 (2019), pp. 234-241.

10.1016/j.ijmedinf.2019.06.007
64

J.K. Tsai, C.C. Hsu, W.Y. Wang, and S.K. Huang, Deep Learning-Based Real-Time Multiple-Person Action Recognition System. Sensors. 20(17) (2020), 4758.

10.3390/s2017475832842485PMC7506925
65

X. Zhang, Y. Yu, F. Xiong, and L. Luo, Prediction of daily blood sampling room visits based on ARIMA and SES Model. Computational and Mathematical Methods in Medicine. 2020(1) (2020), 1720134.

10.1155/2020/172013432963583PMC7486646
66

R.D. Devyania, S.Y. Jewanc, U. Bansal, and X. Denge, Strategic impact of artificial intelligence on the human resource management of the Chinese healthcare industry induced due to COVID-19. IETI Transactions on Economics and Management. 1(1) (2020), pp. 19-33.

67

J. Lieslehto, N. Rantanen, L.M.A. Oksanen, S.A. Oksanen, A. Kivimäki, S. Paju, M. Pietiäinen, L. Lahdentausta, P. Pussinen, V. Anttila, L. Lehtonen, T. Lallukka, A. Geneid, and E. Sanmark, A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic. Scientific Reports. 12(1) (2022), 8055.

10.1038/s41598-022-12107-635577884PMC9109448
68

Y. Zhang and E. Qi, Happy work: Improving enterprise human resource management by predicting workers’ stress using deep learning. Plos one. 17(4) (2022), e0266373.

10.1371/journal.pone.026637335417484PMC9007354
69

E. Meddeb, The Human Resource Management challenge of predicting employee turnover using machine learning and system dynamics. In BIR Workshops. (2021), pp. 184-196.

70

N. Pourkhodabakhsh, M.M. Mamoudan, and A. Bozorgi-Amiri, Effective machine learning, meta- heuristic algorithms and multi-criteria decision making to minimizing human resource turnover. Applied Intelligence. 53(12) (2023), pp. 16309- 16331.

10.1007/s10489-022-04294-636531972PMC9734781
71

H. Zhu, Research on human resource recommendation algorithm based on machine learning. Scientific Programming. 2021(1) (2021), 8387277.

10.1155/2021/8387277
72

S. Hacking, ChatGPT and Medicine: Together We Embrace the AI Renaissance. JMIR Bioinform. Biotechnol. 5 (2024), e52700.

10.2196/5270038935938PMC11135232
73

K. Zhang, X. Meng, X. Yan, J. Ji, J. Liu, H. Xu, H. Zhang, D. Liu, J. Wang, X. Wang, J. Gao, Y. Wang, C. Shao, W. Wang, J. Li, M.Q. Zheng, Y. Yang, and Y.D. Tang, Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine. J. Med. Internet Res. 27 (2025), e59069.

10.2196/5906939773666PMC11751657
74

S. Kumar, S.K. Gupta, V. Kumar, M. Kumar, M.K. Chaube, and N.S. Naik, Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19. Computers and Electrical Engineering. 103 (2022), 108396.

10.1016/j.compeleceng.2022.10839636160764PMC9485428
75

Y. Sahashi, M. Vukadinovic, F. Amrollahi, H. Trivedi, J. Rhee, J. Chen, S. Cheng, D. Ouyang, and A.C. Kwan, Opportunistic Screening of Chronic Liver Disease with Deep-Learning–Enhanced Echocardiography. NEJM AI. 2 (2025), AIoa2400948.

10.1056/AIoa2400948
76

A. Aggarwal, M. Chakradar, M.S. Bhatia, M. Kumar, T. Stephan, S.K. Gupta, S.H. Alsamhi, and H. AL-Dois, COVID-19 risk prediction for diabetic patients using fuzzy inference system and machine learning approaches. Journal of Healthcare Engineering. 2022 (2022), pp. 1-10.

10.1155/2022/409695035368915PMC8974235
77

D. Whicher, M. Ahmed, S. Siddiqui, I. Adams, C. Grossman, and K. Carman, Health data sharing to support better outcomes. 2020, Washington, DC: National Academy of Medicine.

10.17226/27110
78

D.R. Hernández-Galdamez, M.Á. González-Block, D.K. Romo-Dueñas, R. Lima-Morales, I.A. Hernández- Vicente, M. Lumbreras-Guzman, and P. Mendez- Hernandez, Increased risk of hospitalization and death in patients with COVID-19 and pre-existing noncommunicable diseases and modifiable risk factors in Mexico. Archives of Medical Research. 51(7) (2020), pp. 683-689.

10.1016/j.arcmed.2020.07.00332747155PMC7375298
79

K. Alshehhi, S.B. Zawbaa, A.A. Abonamah, and M.U. Tariq, Employee retention prediction in corporate organizations using machine learning methods. Academy of Entrepreneurship Journal. 27 (2021), pp. 1-23.

80

R. Gupta, M.A. Alam, and P. Agarwal, Modified support vector machine for detecting stress level using EEG signals. Computational Intelligence and Neuroscience. 2020(1) (2020), 8860841.

10.1155/2020/886084132802030PMC7416233
81

A. Gregoriades and A. Sutcliffe, Workload prediction for improved design and reliability of complex systems. Reliability Engineering & System Safety. 93(4) (2008), pp. 530-549.

10.1016/j.ress.2007.02.001
82

A. Cribb, Health and the good society: setting healthcare ethics in social context. OUP Oxford. (2005).

10.1093/0199242739.001.0001
83

S.Z. El Mestari, G. Lenzini, and H. Demirci, Preserving data privacy in machine learning systems. Computers & Security. 137 (2024), 103605.

10.1016/j.cose.2023.103605
84

S.K. Thethi, Machine learning models for cost- effective healthcare delivery systems: A global perspective. Digital Transformation in Healthcare 5.0: Volume 1: IoT, AI and Digital Twin, 199. (2024).

10.1515/9783111327853-008
85

M.J. Rahim, M.I.I. Rahim, A. Afroz, and O. Akinola, Cybersecurity Threats in Healthcare IT: Challenges, Risks, and Mitigation Strategies. Journal of Artificial Intelligence General Science. 6(1) (2024), pp. 438-462.

10.60087/jaigs.v6i1.268
86

M. Badawy, N. Ramadan, and H.A. Hefny, Healthcare predictive analytics using machine learning and deep learning techniques: a survey. Journal of Electrical Systems and Inf Technol. 10 (2023), 40.

10.1186/s43067-023-00108-y
87

S.U.R. Khan, S. Asif, M. Zhao, W. Zou, Y. Li, and X. Li, Optimized deep learning model for comprehensive medical image analysis across multiple modalities. Neurocomputing. 619 (2025), 129182.

10.1016/j.neucom.2024.129182
Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 16
  • No :4
  • Pages :461-476
  • Received Date : 2025-09-13
  • Accepted Date : 2025-10-20
  • DOI :https://doi.org/10.22712/susb.20250031
Journal Informaiton International Journal of Sustainable Building Technology and Urban Development International Journal of Sustainable Building Technology and Urban Development
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