Intelligent Hospital Management System Using Machine Learning for Patient Criticality Prediction
Keywords:
Hospital Management System (HMS), Machine Learning (ML), Historical Data, Medical History, Predictive Analysis, User-Friendly InterfaceAbstract
This project is a Hospital Management System in Python with Machine Learning enhancement to improve the hospital’s operation and patient care. The system has the basic functionalities like registration of patients, maintaining their records and monitoring their real-time status through secure and friendly interface. Its main innovation is the Machine Learning module that predicts the patient criticality level at the time of admission. The system then classifies the patients into low, moderate, and high-risk categories by employing supervised learning algorithms trained on historical medical data such as vital signs and patient history. Such predictive power allows healthcare professionals to make faster, data-driven decisions, ensuring immediate attention for critical patients. Moreover, the system can aid in intelligent ward allocation by prediction of risk levels, available beds and ward specialisation. This makes it possible to optimise hospital resources, reduce delays and to prevent unnecessary patient transfers. Backend is designed to ensure data security and efficient processing, while interface is designed to provide easy access to the hospital staff. The system cuts down significantly on the workload by automating administrative tasks and incorporating predictive analytics, which in turn enhances operational efficiency and overall quality of patient care.
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I. Nayak, G. Radha, R. M. M. Shareef, T. E, K. Senthamilselvan and J. V, “Enhancing Student Performance through Adaptive Knowledge Assessment: A Bayesian Optimization Approach,” 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India, 2023.
M. C. Jobin Christ, D. Kalaiyarasi, J. G, K. Senthamilselvan, D. Kirubakaran and N. S. Gowri Ganesh, “PoBTx(Proof of Block and Transaction): An Efficient Consensus Algorithm for IoT Business Blockchain,” 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, 2023.
J. Cao, G. Bhuvaneswari, T. Arumugam, and A. B. R, "The digital edge: Examining the relationship between digital competency and language learning outcomes," Frontiers in Psychology, vol. 14, Jun. 2023.
J. Rehman, M. Kashif, and T. Arumugam, "From the land of Gama: Event attachment scale (EAS) development exploring fans’ attachment and their intentions to spectate at traditional gaming events," International Journal of Event and Festival Management, vol. 14, no. 3, pp. 363–379, Jun. 2023.
K. U. Kiran and T. Arumugam, "Role of programmatic advertising on effective digital promotion strategy: A conceptual framework," Journal of Physics: Conference Series, vol. 1716, p. 012032, Dec. 2020.
M. A. Sanjeev, A. Thangaraja, and P. K. S. Kumar, "Multidimensional scale of perceived social support: Validity and reliability in the Indian context," International Journal of Management Practice, vol. 14, no. 4, p. 472, 2021.
M. A. Sanjeev, S. Khademizadeh, T. Arumugam, and D. K. Tripathi, "Generation Z and intention to use the digital library: Does personality matter?," The Electronic Library, vol. 40, no. 1/2, pp. 18–37, Dec. 2021.
S. Gupta, N. Pande, T. Arumugam, and M. A. Sanjeev, "Reputational impact of COVID‐19 pandemic management on World Health Organization among Indian public health professionals," Journal of Public Affairs, Oct. 2022.
S. Hameed, S. Madhavan, and T. Arumugam, "Is consumer behaviour varying towards low and high involvement products even sports celebrity endorsed?," International Journal of Scientific & Technology Research, vol. 9, no. 3, Mar. 2020. [Online]. Available: https://www.ijstr.org/final-print/mar2020/Is-Consumer-Behaviour-Varying-Towards-Low-And-High-Involvement-Products-Even-Sports-Celebrity-Endorsed.pdf
S. Verma, N. Garg, and T. Arumugam, "Being ethically resilient during COVID-19: A cross-sectional study of Indian supply chain companies," The International Journal of Logistics Management, Aug. 2022.
T. Arumugam, B. L. Lavanya, V. Karthik, K. Velusamy, U. K. Kommuri, and D. Panneerselvam, "Portraying women in advertisements: An analogy between past and present," The American Journal of Economics and Sociology, vol. 81, no. 1, pp. 207–223, Jan. 2022.
T. Arumugam, B. Subramaniam, B. Jayakrishnan, V. Asi, M. Reddy, and Ranganathan, "Financial reengineering perspectives of Government of India with respect to time series effect and performance of sovereign gold bond," Accessed: Aug. 06, 2024. [Online]. Available: https://www.ijstr.org/final-print/mar2020/Financial-Reengineering-Perspectives-Of-Government-Of-India-With-Respect-To-Time-Series-Effect-And-Performance-Of-Sovereign-Gold-Bond.pdf
T. Arumugam, K. M. Ashifa, V. Vinayagalakshmi, U. Kiran, and S. Ramya, "Big Data in Driving Greener Social Welfare and Sustainable Environmental Management," Advances in Business Information Systems and Analytics Book Series, pp. 328–343, Dec. 2023.
T. Arumugam, M. A. Sanjeev, R. K. Mathai, S. R. Boselin Prabhu, R. Balamourougane, and T. Jarin, "An empirical verification of the proposed distributor marketing intelligence system model," International Journal of Business Information Systems, vol. 45, no. 4, pp. 454–473, Jan. 2024.
T. Arumugam, R. Arun, R. Anitha, P. L. Swerna, R. Aruna, and V. Kadiresan, "Advancing and methodizing artificial intelligence (AI) and socially responsible efforts in real estate marketing," Advances in Business Information Systems and Analytics Book Series, pp. 48–59, Dec. 2023.
T. Arumugam, R. Arun, S. Natarajan, K. K. Thoti, P. Shanthi, and U. K. Kommuri, "Unlocking the Power of Artificial Intelligence and Machine Learning in Transforming Marketing as We Know It," Advances in Business Information Systems and Analytics Book Series, pp. 60–74, Dec. 2023.
T. Arumugam, R. Mathai, K. Balasubramanian, Renuga K., M. Rafiq, and V. Kalyani, "The mediating effect of customer intimacy on electronic word of mouth (eWOM) in social networking sites on buying intention," Zenodo (CERN European Organization for Nuclear Research), Sep. 2021.
S. N. Prasad, K. Senthamil Selvan, B. L. Dhevi, and A. N. Kumar, “Computation on scheduling attacks and optimal resource utilisation in wireless sensor networks via optimisation techniques,” International Journal of Internet Protocol Technology, vol. 16, no. 3, pp. 131–137, 2023.
S. Narasimha Prasad, K. Senthamil Selvan, and B. L. Dhevi, “Intrusion Detection System in Wireless Sensor Networks and Fair Resource Allocation Using Geometric Deep Learning Techniques,” Wirel. Pers. Commun., vol. 123, no. 4, pp. 3401–3412, 2022.
G. K. Kamalam, S. Joshi, M. Maheshwari, K. Senthamil Selvan, S. S. Jamal, S. Vairaprakash, and M. Alhassan, “Augmented Reality-Centered Position Navigation for Wearable Devices with Machine Learning Techniques,” J. Healthcare Eng., vol. 2022, no. 1, pp. 1–10, 2022.
R. Dhaya, U. U. J. Ujwal, T. Sharma, P. Singh, R. Kanthavel, K. Senthamil Selvan, and D. Krah, “Energy-Efficient Resource Allocation and Migration in Private Cloud Data Centre,” Wirel. Commun. Mobile Comput., vol. 2022, no. 1, pp. 1–13, 2022.
K. V. N. Kavitha, S. Ashok, A. L. Imoize, S. Ojo, K. Senthamil Selvan, T. A. Ahanger, and M. Alhassan, “On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals,” J. Healthcare Eng., vol. 2022, no. 1, pp. 1–11, 2022.
B. Lakshmi Dhevi, K. Vishvaksenan, K. Senthamil Selvan, and A. Rajalakshmi, “Patient Monitoring System Using Cognitive Internet of Things,” Journal of Medical Systems, vol. 42, no. 11, pp. 1–9, 2018.
K. Senthamil Selvan and B. L. Dhevi, “Telemedicine Consultation Using ARM for Monitoring Vital Signs of Heart Patients at Home,” J. Pure Appl. Microbiol., vol. 9, no. 8, pp. 135–138, 2015.
I. Ganie and S. Jagannathan, “Online continual safe reinforcement learning–based optimal control of mobile robot formations,” Proc. 2024 IEEE Conf. Control Technol. Appl. (CCTA), Newcastle upon Tyne, U.K., 2024, pp. 519–524.
I. Ganie and S. Jagannathan, “Online learning-driven human intent estimation and control for human–robot interaction,” Proc. 2025 Amer. Control Conf. (ACC), Denver, CO, USA, 2025, pp. 5160–5165.
I. Ganie and S. Jagannathan, “Online lifelong optimal tracking control of nonlinear continuous-time strict-feedback systems using deep neural networks,” Neural Netw., vol. 191, 107793, 2025.
I. Ganie and S. Jagannathan, “Optimal trajectory tracking of uncertain nonlinear continuous-time strict-feedback systems with dynamic constraints,” Int. J. Control, pp. 1–15, 2024.
J. R. Rogers, Y. Wang, N. F. Khan, K. Mott, V. K. Nomula, D. Wang, P. C. Fiduccia, M. Burcu, and X. Liu, “Landscape assessment of clone-censor-weight methodology application in real-world data studies: A scoping review,” in Proceedings of the Pharmacoepidemiology and Drug Safety Conference, vol. 33, pp. 424–424, Nov. 1, 2024.
