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International Research Journal of Medical Sciences and Health Care

Open Access Peer Review International
Open Access

Artificial Intelligence Enabled Healthcare Analytics for Patient Risk Assessment and Prediction

DOI https://doi.org/10.55640/irjmshc-v03i07-02
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Dr. Hemant N. Patel 1 ,
4 Computer Engineering, Sankalchand Patel College of Engineering, Sankalchand Patel University, Visnagar, India

Abstract

Healthcare analytics systems based on artificial intelligence (AI) have been developed to enable early risk assessment and personalized patient treatment, thanks to the increasing rise of electronic health records (EHRs). Pressure ulcers (PUs) are a major hospital-acquired complication that require timely identification and preventive interventions. This study proposes an AI-enabled healthcare analytics framework for personalized patient risk assessment and pressure ulcer prediction using the MIMIC-IV clinical dataset. The proposed framework includes data extraction, preprocessing, missing value handling, feature engineering, normalization, and feature selection using Random Forest-based feature importance analysis. Clinically relevant features, including demographic characteristics, ICU-related parameters, vital signs, laboratory measurements, Braden scale factors, and diagnosis information, were utilized for predictive modeling. Decision Tree (DT) and Light Gradient Boosting Machine (LightGBM) are two ML models that were fine-tuned using hyperparameter tweaking to make better risk predictions. The LightGBM model was analysed using explainable AI based on SHAP (SHapley Additive exPlanations) to identify the clinical factors that contribute to pressure ulcer risk prediction. The experimental results demonstrate that the proposed LightGBM model outperformed traditional machine learning approaches, achieving 96.1% accuracy (ACC), 94.8% precision (PRE), 91.9% recall (REC), 93.1% F1-score (F1), and 99.04% ROC-AUC on average. To improve model transparency, the SHAP study identified important clinical variables linked to pressure ulcer formation. The suggested architecture offers a dependable method for pressure ulcer prediction and may find use in intelligent clinical decision-support systems and real-time healthcare monitoring.

Keywords

Artificial intelligence, Healthcare systems, Patient risk prediction

References

R. Snehamrutha, “Patient Engagement Strategies in Community Pharmacies and their Effect on Vaccination Uptake and Medication Synchronizations,” ESP J. Eng. Technol. Adv., vol. 3, no. 3, pp. 163–173, 2023, doi: 10.56472/25832646/JETA-V3I7P120.
A. Warrier, “Machine Learning-Enhanced Data Quality Validation for Electronic Health Record Integration,” Int. J. Sci. Technol., vol. 11, no. 1, pp. 1–8, Feb. 2020, doi: 10.71097/IJSAT.v11.i1.8387.
M. Patel and V. Chaturvedi, “A survey on artificial intelligence techniques for disease prediction in healthcare,” ESP J. Eng. Technol. Adv., vol. 5, no. 4, pp. 201–210, 2025.
M. Mittal, “AI IN HEALTHCARE: THE NEXT FRONTIER IN MEDICAL DIAGNOSIS,” Int. J. Inf. Technol. Manag. Inf. Syst., vol. 16, no. 2, pp. 1448–1455, Mar. 2025, doi: 10.34218/IJITMIS_16_02_091.
S. Mukherjee, “Machine Learning-Enabled Classification of Diabetes Using Clinical via Lifestyle Health Data,” in 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), IEEE, Feb. 2026, pp. 1–6. doi: 10.1109/ICAIC67076.2026.11395709.
S. A. Devanira Poovaiah, M. Sankaran, G. Namazov, N. Jooluri, and M. Mukhlisa, “Alzheimer’s Disease Detection from MRI Scans using Deep Hybrid Architecture with Metaheuristic Hyperparameter Tuning,” in 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3), IEEE, Jul. 2025, pp. 1–7. doi: 10.1109/ISAC364032.2025.11156608.
S. Mukherjee, “AI-Driven Deep Learning System for Alzheimer’s Disease (AD) Detection Via MRI Imaging,” in 2026 International Conference on Intelligent Computing, Networks, and Security (IC-ICNS), Bhubaneswar, India: IEEE, 2026, pp. 1–6, March. doi: 10.1109/IC-ICNS68863.2026.11537765.
S. R. Chanthati, “Integrating-Large-Language-Models-LLMs-in-Cognitive-Medical-Robotics,” Int. Conf. AI Robot. (AIR 2025), 2025, doi: 10.5281/zenodo.20316245.
V. Kumar, S. K. Madishetty, and G. N. Maddipatla, “Intelligent Surgical Tables: Real-Time Patient Monitoring and Autonomous Adjustment Using IoT, Cloud Computing, and Cybersecurity,” J. Comput. Anal. Appl., vol. 31, no. 02, Feb. 2023, doi: 10.48047/jocaaa.2023.31.02.50.
S. Mahmud, K. K. Mohammed, V. R. Jain, and S. A. Shah, “Artificial Intelligence-Driven Predictive Models for Identifying Risk Factors of Chronic Diseases,” in 2026 14th International Symposium on Digital Forensics and Security (ISDFS), Boston, MA, USA: IEEE, Mar. 2026, pp. 01–06. doi: 10.1109/ISDFS69419.2026.11459003.
A. Warrier, “Machine Learning-Driven Pandemic Response-Real-Time Epidemiological Data Integration for National Health Emergency Preparedness,” Int. J. Artif. Intell. Data Sci. Mach. Learn., vol. 6, no. 3, pp. 73–79, 2025.
R. Snehamrutha, “Development and In-Vitro Evaluation of a Sustained-Release Transdermal Patch for Improved Patient Adherence,” ESP Int. J. Adv. Sci. Technol., vol. 2, no. 4, pp. 34–43, 2024, doi: 10.56472/25839233/IJAST-V2I4P105.
S. K. Anumula, V. Tamilmani, A. A. Singh, D. Rajendran, and V. D. Namburi, “Deep Learning–Based CNN Model for Automated Detection of Pneumonia from Chest X-Ray Images,” Int. Conf. Innov. Comput. Commun., pp. 360–377, 2026.
R. V. S. S. B. R, S. S. Harakannanavar, Z. A. Alsalami, M. S, and G. Vijayakumari, “Early Detection of Sepsis by using Attention based Recurrent Neural Networks with Shapley Additive Exp lanations,” in 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS), IEEE, Nov. 2024, pp. 1–5. doi: 10.1109/ICIICS63763.2024.10860199.
K. K. Mohammed, “A Survey on Digital Health Care Data Analysis Techniques for Developing Machine Learning Models,” Int. J. Sci. Eng. Technol., vol. 13, no. 5, October, pp. 1–6, 2025, doi: :10.5281/zenodo.17339813.
M. N. Paralath, “Towards Interpretable and Attentive Deep Visual Perception Through Iterative Refinement in Convolutional Neural Networks,” in 2025 International Conference on Advances in Next-Gen Computer Science (ICANCS), IEEE, Nov. 2025, pp. 1–6. doi: 10.1109/ICANCS65819.2025.11376668.
G. Rajaram, K. A. Davamani, V. Janakiraman, M. Amanullah, P. Princy Magdaline, and V. G. Sivakumar, “Advanced Machine Learning Ensemble Approach with Novel Feature Engineering for Heart Attack Prediction: A Comprehensive Cardiovascular Risk Assessment Model,” in 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN), IEEE, Apr. 2026, pp. 1–6. doi: 10.1109/QPAIN69676.2026.11546608.
A. J. D. Acosta, K. J. B. Arellano, S. D. E. Seblante, M. S. A. Magboo, and V. P. C. Magboo, “Machine Learning Models with Explainable AI for Preoperative Frailty Risk Prediction in Elderly Patients with Hepatocellular Carcinoma,” in 2026 4th International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), IEEE, Apr. 2026, pp. 1–6. doi: 10.1109/AIMLA67915.2026.11522435.
F. Salehi, S. Bayat, G. Schett, A. Kleyer, T. Altstidl, and B. M. Eskofier, “ExSMART-PreRA: Explainable Survival and Risk Assessment Using Machine Learning for Time Estimation in Preclinical Rheumatoid Arthritis,” IEEE J. Biomed. Heal. Informatics, vol. 29, no. 8, pp. 6017–6028, Aug. 2025, doi: 10.1109/JBHI.2025.3554364.
O. Lohaj, J. Paralič, and T. Klein, “Machine Learning for COVID-19 Risk Assessment in Cardiovascular Patients,” in 2025 International Conference on Emerging eLearning Technologies and Applications (ICETA), IEEE, Nov. 2025, pp. 524–530. doi: 10.1109/ICETA67772.2025.11280055.
M. Jin, S. Nan, and S. Fu, “A Risk Prediction System Based on Machine Learning for Acute Ischemic Stroke in Patients with Atrial Fibrillation,” in 2024 17th International Convention on Rehabilitation Engineering and Assistive Technology (i-CREATe), IEEE, Aug. 2024, pp. 1–6. doi: 10.1109/i-CREATe62067.2024.10776121.
D. Balakrishnan, U. Mariappan, B. Sai Prudhvi, P. Saikumar, B. Mohan Mahesh, and P. Mani Sai, “Alzheimer Disease Prediction Using Recursive Feature Elimination and Artificial Neural Network,” in 2023 International Conference on Network, Multimedia and Information Technology (NMITCON), IEEE, Sep. 2023, pp. 1–6. doi: 10.1109/NMITCON58196.2023.10276170.
N. Prakash, M. Mahesh, and P. Gouthaman, “Cardiovascular Disease Risk Assessment using Machine Learning,” in 6th International Conference on Inventive Computation Technologies, ICICT 2023 - Proceedings, 2023. doi: 10.1109/ICICT57646.2023.10133957.
R. Johnson, A.; Bulgarelli, L.; Pollard, T.; Horng, S.; Celi, L.A.; Mark IV, “Mimic-Iv (Version 0.4),” PhysioNet.
V. Chaturvedi, “Disease Diagnostic Systems based on AI-Applications in Healthcare: Models, Challenges, and Future Directions,” Int. J. Emerg. Res. Eng. Technol., vol. 6, no. 4, pp. 207–217, Dec. 2025, doi: 10.63282/3050-922X.IJERET-V6I4P125.
K. Mohapatra, J. Mishra, S. R. Pattanaik, A. Pati, A. Panigrahi, and B. Sahu, “EFSMLHA: Ensembled Feature Selected Machine Learning Hybrid Approaches for Heart Disease Prediction,” in 2025 International Conference in Advances in Power, Signal, and Information Technology (APSIT), 2025, pp. 1–6. doi: 10.1109/APSIT63993.2025.11086260.
B. Sahu, J. Mishra, S. Parveen, B. Das, A. Panigrahi, and A. Pati, “STIR-MO: An Enhanced Hybrid Cancer Predictive Model,” in 2025 International Conference on Intelligent and Cloud Computing (ICoICC), 2025, pp. 1–6. doi: 10.1109/ICoICC64033.2025.11052206.
A. Gabitashvili and P. Kellmeyer, “Predicting Length of Stay in Neurological ICU Patients Using Classical Machine Learning and Neural Network Models: A Benchmark Study on MIMIC-IV,” no. 1, 2025.
M. Chang, Z. Xu, L. Xu, C. Li, X. Song, and L. Niu, “Machine learning model of clinical laboratory data for 30-day mortality of patients with hodgkin’s lymphoma in ICU: a retrospective study based on MIMIC-IV database,” Clin. Exp. Med., vol. 26, no. 1, p. 53, Dec. 2025, doi: 10.1007/s10238-025-01973-9.
H. Chen et al., “Machine learning prediction and interpretability analysis of high-risk chest pain: a study from the MIMIC-IV database,” Front. Physiol., vol. 16, Jun. 2025, doi: 10.3389/fphys.2025.1594277.
H. Huynh, T. Cao, and H. Tran, “Machine Learning-Based Diabetes Classification Using Vital Signs and Clinical Information from the MIMIC-IV Dataset,” Sci. J. Comput. Sci., vol. 2, no. 2, pp. 148–159, Mar. 2026, doi: 10.64539/sjcs.v2i2.2026.439.

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