The Role of Machine Learning in Managing and Organizing Healthcare Records


  • Ahmed Mohammed Alghamdi Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
  • Mahmoud Ahmad Al-Khasawneh School of Computing, Skyline University College, University City Sharjah, 1797, Sharjah, UAE | Applied Science Research Center. Applied Science Private University, Amman, Jordan
  • Ala Alarood College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
  • Eesa Alsolami College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
Volume: 14 | Issue: 2 | Pages: 13695-13701 | April 2024 |


With the exponential growth of medical data, Machine Learning (ML) algorithms are becoming increasingly important to the management and organization of healthcare information. This study aims to explore the role that ML can play in optimizing the management and organization of healthcare records, by identifying the challenges, advantages, and limitations associated with this technology. Consequently, the current study will contribute to the understanding of how ML might be applied to the healthcare industry in a variety of circumstances. Using the findings of this study, healthcare professionals, researchers, and policymakers will be able to make informed decisions regarding the adoption and implementation of ML techniques for regulating healthcare records. The findings of this paper revealed that ML can play an important role in efficiently directing and classifying healthcare records using different perspectives.


machine learning, healthcare records, literature review methodology


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K. C. Rath, A. Khang, and D. Roy, "The Role of Internet of Things (IoT) Technology in Industry 4.0 Economy," in Advanced IoT Technologies and Applications in the Industry 4.0 Digital Economy, 1st ed., Boca Raton, FL, USA: CRC Press, 2024, pp. 1–28.

A. E. Yahya, A. Gharbi, W. M. S. Yafooz, and A. Al-Dhaqm, "A Novel Hybrid Deep Learning Model for Detecting and Classifying Non-Functional Requirements of Mobile Apps Issues," Electronics, vol. 12, no. 5, Jan. 2023, Art. no. 1258.

W. A. H. Altowayti et al., "The Role of Conventional Methods and Artificial Intelligence in the Wastewater Treatment: A Comprehensive Review," Processes, vol. 10, no. 9, Sep. 2022, Art. no. 1832.

S. Messinis, N. Temenos, N. E. Protonotarios, I. Rallis, D. Kalogeras, and N. Doulamis, "Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review," Computers in Biology and Medicine, vol. 170, Mar. 2024, Art. no. 108036.

M. Rasool, N. A. Ismail, A. Al-Dhaqm, W. M. S. Yafooz, and A. Alsaeedi, "A Novel Approach for Classifying Brain Tumours Combining a SqueezeNet Model with SVM and Fine-Tuning," Electronics, vol. 12, no. 1, Jan. 2023, Art. no. 149.

H. Askr, E. Elgeldawi, H. Aboul Ella, Y. A. M. M. Elshaier, M. M. Gomaa, and A. E. Hassanien, "Deep learning in drug discovery: an integrative review and future challenges," Artificial Intelligence Review, vol. 56, no. 7, pp. 5975–6037, Jul. 2023.

K. B. Vikhyath and N. A. Prasad, "Combined Osprey-Chimp Optimization for Cluster Based Routing in Wireless Sensor Networks: Improved DeepMaxout for Node Energy Prediction," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12314–12319, Dec. 2023.

Y. Zhang and Z. Dong, "Medical Imaging and Image Processing," Technologies, vol. 11, no. 2, Apr. 2023, Art. no. 54.

Z. Ji et al., "Lung Nodule Detection in Medical Images Based on Improved YOLOv5s," IEEE Access, vol. 11, pp. 76371–76387, 2023.

C. Wang, X. Lv, M. Shao, Y. Qian, and Y. Zhang, "A novel fuzzy hierarchical fusion attention convolution neural network for medical image super-resolution reconstruction," Information Sciences, vol. 622, pp. 424–436, Apr. 2023.

B. D. Katzman, C. B. van der Pol, P. Soyer, and M. N. Patlas, "Artificial intelligence in emergency radiology: A review of applications and possibilities," Diagnostic and Interventional Imaging, vol. 104, no. 1, pp. 6–10, Jan. 2023.

P. Chakraborty, T. Chandrapragasam, A. Arunachalam, and S. Rafiammal, "Artificial Intelligence-based Oral Cancer Screening System using Smartphones: Oral cancer screening system," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12054–12057, Dec. 2023.

A. Salau, N. A. Nwojo, M. M. Boukar, and O. Usen, "Advancing Preauthorization Task in Healthcare: An Application of Deep Active Incremental Learning for Medical Text Classification," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12205–12210, Dec. 2023.

J. F. Wolfswinkel, E. Furtmueller, and C. P. M. Wilderom, "Using grounded theory as a method for rigorously reviewing literature," European Journal of Information Systems, vol. 22, no. 1, pp. 45–55, Jan. 2013.

I. U. Onwuegbuzie, S. A. Razak, I. F. Isnin, T. S. J. Darwish, and A. Al-dhaqm, "Optimized backoff scheme for prioritized data in wireless sensor networks: A class of service approach," PLOS ONE, vol. 15, no. 8, Jul. 2020, Art. no. e0237154.

H. Shamshad, F. Ullah, A. Ullah, V. R. Kebande, S. Ullah, and A. Al-Dhaqm, "Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions," IEEE Access, vol. 11, pp. 122205–122220, 2023.

M. Q. Mohammed et al., "Review of Learning-Based Robotic Manipulation in Cluttered Environments," Sensors, vol. 22, no. 20, Jan. 2022, Art. no. 7938.

B. Alsinglawi et al., "An explainable machine learning framework for lung cancer hospital length of stay prediction," Scientific Reports, vol. 12, no. 1, Jan. 2022, Art. no. 607.

A. Hussain, K. Farooq, B. Luo, and W. Slack, "A Novel Ontology and Machine Learning Inspired Hybrid Cardiovascular Decision Support Framework," in IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa, Dec. 2015, pp. 824–832.

L. S. Kumar and A. Padmapriya, "Disease Information Extraction from Healthcare Records using CTA Matrix," Australian Journal of Basic and Applied Sciences, vol. 10, no. 2, pp. 141–149, 2016.

N. V. Pardakhe and V. M. Deshmukh, "Machine Learning and Blockchain Techniques Used in Healthcare System," in IEEE Pune Section International Conference, Pune, India, Dec. 2019, pp. 1–5.

P. Pandey and R. Litoriya, "Securing and authenticating healthcare records through blockchain technology," Cryptologia, vol. 44, no. 4, pp. 341–356, Jul. 2020.

A. W. Kempa-Liehr et al., "Healthcare pathway discovery and probabilistic machine learning," International Journal of Medical Informatics, vol. 137, May 2020, Art. no. 104087.

A. Taylor, R. Kleiman, S. Hebbring, P. Peissig, and D. Page, "High-Throughput Approach to Modeling Healthcare Costs Using Electronic Healthcare Records." arXiv, Jun. 01, 2022.

A. Datta et al., "‘Black Box’ to ‘Conversational’ Machine Learning: Ondansetron Reduces Risk of Hospital-Acquired Venous Thromboembolism," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 2204–2214, Jun. 2021.

K. P. Arjun and K. S. Kumar, "Machine Learning -A Neoteric Medicine to Healthcare," International Journal on Emerging Technologies, vol. 11, no. 3, pp. 195–201, May 2020.

P. K. Yeng, M. Ali Fauzi, and B. Yang, "Comparative analysis of machine learning methods for analyzing security practice in electronic health records’ logs," in IEEE International Conference on Big Data, Atlanta, GA, USA, Dec. 2020, pp. 3856–3866.

O. Bardhi and B. Garcia Zapirain, "Machine Learning Techniques Applied to Electronic Healthcare Records to Predict Cancer Patient Survivability," Computers, Materials & Continua, vol. 68, no. 2, pp. 1595–1613, 2021.

P. Papadopoulos, W. Abramson, A. J. Hall, N. Pitropakis, and W. J. Buchanan, "Privacy and Trust Redefined in Federated Machine Learning," Machine Learning and Knowledge Extraction, vol. 3, no. 2, pp. 333–356, Jun. 2021.

M. H. Chaithra and S. Vagdevi, "A Detailed Survey Study on Various Issues and Techniques for Security and Privacy of Healthcare Records," in Intelligent Sustainable Systems, J. S. Raj, R. Palanisamy, I. Perikos, and Y. Shi, Eds. New York, NY, USA: Springer, 2022, pp. 181–189.

S. Dutta and S. K. Bandyopadhyay, "Diabetes Prediction Using Machine Learning Approaches," in Advanced Prognostic Predictive Modelling in Healthcare Data Analytics, S. Roy, L. M. Goyal, and M. Mittal, Eds. New York, NY, USA: Springer, 2021, pp. 179–202.

D. K. Sharma, D. S. Chakravarthi, R. S. K. Boddu, A. Madduri, M. R. Ayyagari, and Md. Khaja Mohiddin, "Effectiveness of Machine Learning Technology in Detecting Patterns of Certain Diseases Within Patient Electronic Healthcare Records," in Second International Conference in Mechanical and Energy Technology, Greater Noida, India, Oct. 2021, pp. 73–81.

A. Haddad, M. H. Habaebi, Md. R. Islam, N. F. Hasbullah, and S. A. Zabidi, "Systematic Review on AI-Blockchain Based E-Healthcare Records Management Systems," IEEE Access, vol. 10, pp. 94583–94615, 2022.

Z. Wang and J. Sun, "PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning." arXiv, Oct. 11, 2022.

M. Kumar, S. Singhal, S. Shekhar, B. Sharma, and G. Srivastava, "Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning," Sustainability, vol. 14, no. 21, Jan. 2022, Art. no. 13998.

X. Sun, A. Douiri, and M. Gulliford, "Applying machine learning algorithms to electronic health records to predict pneumonia after respiratory tract infection," Journal of Clinical Epidemiology, vol. 145, pp. 154–163, May 2022.

N. Chen et al., "Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms," Frontiers in Public Health, vol. 10, Oct. 2022, Art. no. 984621.

A. Ayub Khan et al., "Healthcare Ledger Management: A Blockchain and Machine Learning-Enabled Novel and Secure Architecture for Medical Industry," Human-centric Computing and Information Sciences, vol. 12, Nov. 2022, Art. no. 55.

D. Tenepalli and N. Thandava Meganathan, "A Review on Machine Learning and Blockchain Technology in E-Healthcare," in 22nd International Conference on Intelligent Systems Design and Applications, Dec. 2022, pp. 338–349.

V. Kumawat, B. Umamaheswari, P. Mitra, and G. Lavania, "Machine Learning for Health Care: Challenges, Controversies, and Its Applications," in Soft Computing: Theories and Applications, R. Kumar, C. W. Ahn, T. K. Sharma, O. P. Verma, and A. Agarwal, Eds. New York, NY, USA: Springer, 2022, pp. 253–261.

E. S. Tumpa and K. Dey, "A Review on Applications of Machine Learning in Healthcare," in 6th International Conference on Trends in Electronics and Informatics, Tirunelveli, India, Apr. 2022, pp. 1388–1392.

T. J. Banks, T. D. Nguyen, J. K. Uhlmann, S. S. Nair, and J. F. Scherrer, "Predicting opioid use disorder before and after the opioid prescribing peak in the United States: A machine learning tool using electronic healthcare records," Health Informatics Journal, vol. 29, no. 2, Apr. 2023, Art. no. 14604582231168826.

S. Gupta, G. F. Nama, and S. Deivasigamani, "Real-Time Monitoring of Patient Activity Using IoT and Machine Learning in Healthcare," International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 7s, pp. 51–57, Jul. 2023.

S. Rani, P. Kumar Pareek, J. Kaur, M. Chauhan, and P. Bhambri, "Quantum Machine Learning in Healthcare: Developments and Challenges," in International Conference on Integrated Circuits and Communication Systems, Raichur, India, Feb. 2023, pp. 1–7.

G. Parashar, A. Chaudhary, and D. Pandey, "Machine Learning for Prediction of Cardiovascular Disease and Respiratory Disease: A Review," SN Computer Science, vol. 5, no. 1, Jan. 2024, Art. no. 196.

S. S. Saranya, P. Anusha, S. Chandragandhi, O. Kiran Kishore, N. Phani Kumar, and K. Srihari, "Enhanced decision-making in healthcare cloud-edge networks using deep reinforcement and lion optimization algorithm," Biomedical Signal Processing and Control, vol. 92, Jun. 2024, Art. no. 105963.


How to Cite

A. M. Alghamdi, M. A. Al-Khasawneh, A. Alarood, and E. Alsolami, “The Role of Machine Learning in Managing and Organizing Healthcare Records”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13695–13701, Apr. 2024.


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