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Open Access Research Article

Machine learning-driven data analytics for improved diagnostic accuracy, treatment efficacy, and real-time monitoring in smart health care

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* Corresponding author · click or hover a name for details

pp. 2291–2317Vol. 46Issue 7October 2025DOI: 10.47974/JIOS-2031XML
Received:
10 Jun 2025
Published Online:
31 Oct 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2031
Pages:
2291–2317

Abstract

This research proposed a complete healthcare data analytics context leveraging machine learning (ML) techniques to improve patient results and enable real-time monitoring. Smart healthcare leverages IoT, AI-driven predictive analytics, real-time anomaly detection, personalized recommendations, remote monitoring, optimized resource allocation, and secure data management to enhance patient outcomes and enhance healthcare efficiency. Moreover, the proposed method integrates feature selection approaches, predictive modeling, and optimization techniques to advance decision-making in vital healthcare scenarios. Moreover, Principal Component Analysis (PCA) and Genetic Algorithm (GA) were implemented for feature selection, while Random Forest and Gradient Boosting were applied for predictive modeling. The results establish high predictive accuracy, with performance metrics ranging from 85% to 98%, showcasing the efficiency. Furthermore, Difference detection techniques like Isolation Forest were utilized for real-time monitoring, providing actionable insights.The researchsubsidizes optimizing healthcare resource allocation through Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) along with comparative analysis favouring PSO for faster convergence. At last, the findings support the combination of machine (ML) learning contexts in healthcare for improved patient administration and operational effectiveness.

Keywords

Subject Classifications

68T0568W5068W9968T99

References

[1] I. Cano, E. Arismendi, and X. Borrat, “Digital health frameworks,” Digital Respiratory Healthcare (ERS Monograph). Sheffield, European Respiratory Society, pp. 27–37 (2023).
[2] M. Javaid, A. Haleem, R. P. Singh, and R. Suman, “Artificial Intelligence Applications for Industry 4.0: A Literature-Based Study,” J. Ind. Intg. Mgmt., vol. 07, no. 01, pp. 83–111 (Mar. 2022), doi: 10.1142/S2424862221300040.
[3] R. A. Kawsar, “Advancing Patient Care through Advanced AI and ML Techniques: Current Trends and Future Directions in Healthcare,” Journal ID, vol. 9471, pp. 1297 (2024).
[4] S. Asif, W. Yi, S. ur‑Rehman, Q. ul‑Ain, K. Amjad, Y. Yueyang, J. Si, and M. Awais, “Advancements and Prospects of Machine Learning in Medical Diagnostics: Unveiling the Future of Diagnostic Precision,” Archives of Computational Methods in Engineering (June 2024). doi: 10.1007/s11831‑024‑10148‑w.
[5] S. A. Wagan, J. Koo, I. F. Siddiqui, M. Attique, D. R. Shin, and N. M. F. Qureshi, “Internet of medical things and trending converged technologies: A comprehensive review on real-time applications,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, Part B, pp. 9228–9251 (Nov. 2022), doi: 10.1016/j.jksuci.2022.09.005.
[6] M. S. Ibrahim and S. Saber, “Machine learning and predictive analytics: Advancing disease prevention in healthcare,” Journal of Contemporary Healthcare Analytics, vol. 7, no. 1, pp. 53–71 (2023).
[7] B. Y. Kasula, “Harnessing Machine Learning for Personalized Patient Care,” Transactions on Latest Trends in Artificial Intelligence, vol. 4, no. 4, 2023, Accessed: Dec. 21 (2024). [Online]. Available: https://ijsdcs.com/index.php/TLAI/article/view/399
[8] A.-T. Shumba, T. Montanaro, I. Sergi, L. Fachechi, M. De Vittorio, and L. Patrono, “Leveraging IoT-aware technologies and AI techniques for real-time critical healthcare applications,” Sensors, vol. 22, no. 19, pp. 7675 (2022).
[9] A. A. Khan, S. Siddiqui, A. A. Khan, and K. Karam, “Advancing Patient-Centric Care: Harnessing CPS for Smart Hospitals and Healthcare Facilities,” in Intelligent Cyber-Physical Systems for Healthcare Solutions, M. Mittal and J. Narayan, Eds., Singapore: Springer Nature Singapore, pp. 377–399. (2024) doi: 10.1007/978-981-97-8983-2_16.
[10] S. Aminizadeh, M. R. Akbarzadeh‑Talahi, A. Zangiabadi, and A. Rezaie, “Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service,” Artificial Intelligence in Medicine, vol. 149, pp. 102779 (Mar. 2024). doi: 10.1016/j.artmed.2024.102779.
[11] “The first source of healthcare data is heterogeneous... - Google Scholar.” Accessed: Feb. 27 (2025). [Online]. Available: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=The+first+source+of+healthcare+data+is+heterogeneous+and+fragmented+because+they+are+collected+from+many+kinds+of+devices%2C+including+wearable+devices%2C+laboratory+results%2C+medical+imaging%2C+and+electronic+health+records+%28EHRs%29+%28Shaikh+et+al.%2C+2023%29.+&btnG=
[12] A. M. Ali and M. A. Mohammed, “A comprehensive review of artificial intelligence approaches in omics data processing: evaluating progress and challenges,” International Journal of Mathematics, Statistics, and Computer Science, vol. 2, pp. 114–167 (2024).
[13] K. Rasheed, A. Qayyum, M. Ghaly, A. Al-Fuqaha, A. Razi, and J. Qadir, “Explainable, trustworthy, and ethical machine learning for healthcare: A survey,” Computers in Biology and Medicine, vol. 149, pp. 106043 (Oct. 2022), doi: 10.1016/j.compbiomed.2022.106043.
[14] “51. Smith, J., & Moss, G. (2024). Ethical Considerations... - Google Scholar.” Accessed: Feb. 27 (2025). [Online]. Available: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=51.%09Smith%2C+J.%2C+%26+Moss%2C+G.+%282024%29.+Ethical+Considerations+in+Implementing+AI%2FML+for+Regulatory+Compliance.+EasyChair.+https%3A%2F%2Feasychair.org%2Fpublications%2Fpreprint_download%2FSVmC&btnG=
[15] R. G. Goriparthi, “Interpretable Machine Learning Models for Healthcare Diagnostics: Addressing the Black-Box Problem,” Revista de Inteligencia Artificial en Medicina, vol. 13, no. 1, pp. 508–534 (2022).
[16] S. Khanna, S. Srivastava, I. Khanna, and V. Pandey, “Current challenges and opportunities in implementing AI/ML in cancer imaging: Integration, development, and adoption perspectives,” Journal of Advanced Analytics in Healthcare Management, vol. 4, no. 10, pp. 1–25 (2020).
[17] N. Mahmoud, S. El-Sappagh, H. M. El-Bakry, and S. Abdelrazek, “A real-time framework for patient monitoring systems based on a wireless body area network,” Int. J. Comput. Appl, vol. 176, no. 27, pp. 12–21 (2020).
[18] J. J. Bird, J. Kobylarz, D. R. Faria, A. Ekárt, and E. P. Ribeiro, “Cross-Domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG,” IEEE Access, vol. 8, pp. 54789–54801 (2020), doi: 10.1109/ACCESS.2020.2979074.
[19] “Predicting Medical Interventions from Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring | SpringerLink.” Accessed: Feb. 27 (2025). [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-77211-6_33
[20] S. Shukla, “Real-time monitoring and predictive analytics in healthcare: harnessing the power of data streaming,” International Journal of Computer Applications, vol. 185, no. 8, pp. 32–37 (2023).
[21] T. Shaik, X. Tao, H. Xie, L. Li, J. Yong, and Y. Li, “Graph-Enabled Reinforcement Learning for Time Series Forecasting With Adaptive Intelligence,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 4, pp. 2908–2918 (Aug. 2024), doi: 10.1109/TETCI.2024.3398024.
[22] L. Na, A. Ahuja, D. D. Lee, J. Doshi, R. Agrawal, A. Singh, and Z. C. Lipton, “Patient outcome predictions improve operations at a large hospital network,” arXiv preprint arXiv:2305.15629, May 25 (2023). doi: 10.48550/arXiv.2305.15629.
[23] P. S. Deorankar, V. V. Vaidya, N. M. Munot, K. S. Jain, and A. R. Patil, “Optimizing Healthcare Throughput: The Role of Machine Learning and Data Analytics,” in Biosystems, Biomedical & Drug Delivery Systems: Characterization, Restoration and Optimization, S. Kulkarni, A. K. Haghi, and S. Manwatkar, Eds., Singapore: Springer Nature, pp. 225–255 (2024). doi: 10.1007/978-981-97-2596-0_11.
[24] P. Bhambri and A. Khang, “Machine Learning Advancements in E-Health: Transforming Digital Healthcare,” in Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry, IGI Global Scientific Publishing, pp. 174–194 (2024). doi: 10.4018/979-8-3693-2105-8.ch012.
[25] B. Hunter, S. Hindocha, and R. W. Lee, “The Role of Artificial Intelligence in Early Cancer Diagnosis,” Cancers, vol. 14, no. 6, Art. no. 6 (Jan. 2022), doi: 10.3390/cancers14061524.
[26] K. B. Nielsen, M. L. Lautrup, J. K. H. Andersen, T. R. Savarimuthu, and J. Grauslund, “Deep Learning–Based Algorithms in Screening of Diabetic Retinopathy: A Systematic Review of Diagnostic Performance,” Ophthalmology Retina, vol. 3, no. 4, pp. 294–304 (Apr. 2019), doi: 10.1016/j.oret.2018.10.014.
[27] A. Rahman, M. Karmakar, and P. Debnath, “Predictive analytics for healthcare: Improving patient outcomes in the US through Machine Learning,” Revista de Inteligencia Artificial en Medicina, vol. 14, no. 1, pp. 595–624 (2023).
[28] K. E. Henry and H. M. Giannini, “Early warning systems for critical illness outside the intensive care unit,” Critical Care Clinics, vol. 40, no. 3, pp. 561–581 (2024).
[29] Z. Obermeyer and E. J. Emanuel, “Predicting the Future — Big Data, Machine Learning, and Clinical Medicine,” N Engl J Med, vol. 375, no. 13, pp. 1216–1219 (Sep. 2016), doi: 10.1056/NEJMp1606181.
[30] A. P. Ramalingam, S. M. R. Hash, C. T. Hash, R. Srivastava, M. K. Singh, A. Nepolean, and P. Ramu, “Drought tolerance and grain yield performance of genetically diverse pearl millet [Pennisetum glaucum (L.) R. Br.] seed and restorer parental lines,” Crop Science, p. csc2.21271 (May 2024). doi: 10.1002/csc2.21271.
[31] I. D. Mienye, T. G. Swart, and G. Obaido, “Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications,” Information, vol. 15, no. 9, Art. no. 9 (Sep. 2024), doi: 10.3390/info15090517.
[32] H. Jain, M. D. Marsool Marsool, R. M. Odat, H. Noori, J. Jain, Z. Shakhatreh, N. Patel, A. Goyal, S. Gole, and S. Passey, “Emergence of Artificial Intelligence and Machine Learning Models in Sudden Cardiac Arrest: A Comprehensive Review of Predictive Performance and Clinical Decision Support,” Cardiology in Review, pp. 10.1097/CRD.0000000000000708, June 5 (2024). doi: 10.1097/CRD.0000000000000708. 
[33] N. Wong and X. Wang, “miRDB: an online resource for microRNA target prediction and functional annotations,” Nucleic Acids Research, vol. 43, no. D1, pp. D146–D152 (Jan. 2015), doi: 10.1093/nar/gku1104.
[34] M. Moor, B. Rieck, M. Horn, C. R. Jutzeler, and K. Borgwardt, “Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review,” Front. Med., vol. 8 (May 2021), doi: 10.3389/fmed.2021.607952.
[35] A. ADITYA, “Impact of artificial intelligence on business models and their innovation in the healthcare sector,” Oct. 2023, Accessed: Feb. 27 (2025). [Online]. Available: https://www.politesi.polimi.it/handle/10589/209993
[36] Motlaq O. Aldosari, Mohammed Saad S. Aldosari, Ibrahim K. Aldosari, Abdulaziz N. Aldwai, Mohannad A. M. Alhaj, Bandar A. E. Faqeeh, Fawaz Dakhel F. Fah, Saad Awad S. Almarzzouk, Mardi H. Alzahrani, Zaid A. B. Shalhoub, Abdullah M. AlMughiyrah, Abdullah O. W. Alhejaili, Obaid S. H. Aldosari, Naif S. Al‑Shahrani, and Faraj M. G. Alshehri, “The Long‑Term Effects of Continuous Health Monitoring Devices on Patient Outcomes: An Epidemiological Perspective on Infectious Disease,” Egyptian Journal of Chemistry, vol. 67, no. 13, pp. 1439–1447 (Dec. 2024). doi: 10.21608/ejchem.2024.332983.10719. 
[37] T. Mendes, P. J. Cardoso, J. Monteiro, and J. Raposo, “Anomaly detection of consumption in hotel units: A case study comparing isolation forest and variational autoencoder algorithms,” Applied Sciences, vol. 13, no. 1, pp. 314 (2022).
[38] R. Siddalingappa and S. Kanagaraj, “Anomaly detection on medical images using autoencoder and convolutional neural network,” International Journal of Advanced Computer Science and Applications, no. 7, 2021, Accessed: Feb. 28 (2025). [Online]. Available: https://drive.google.com/file/d/1YqcJLPphju3I8IkkM1xBegnK2zfKFF_v/view
[39] J. Mercat, T. Gilles, N. El Zoghby, G. Sandou, D. Beauvois, and G. P. Gil, “Multi-head attention for multi-modal joint vehicle motion forecasting,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 9638–9644 (2020). Accessed: Feb. 28, 2025. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9197340/
[40] T. Al-Shehari, M. Al-Razgan, T. Alfakih, R. A. Alsowail, and S. Pandiaraj, “Insider Threat Detection Model Using Anomaly-Based Isolation Forest Algorithm,” IEEE Access, vol. 11, pp. 118170–118185 (2023), doi: 10.1109/ACCESS.2023.3326750.
[41] “Anomaly-based threat detection in smart health using machine learning | BMC Medical Informatics and Decision Making.” Accessed: Feb. 28 (2025). [Online]. Available: https://link.springer.com/article/10.1186/s12911-024-02760-4
[42] A. A. Abdellatif, N. Mhaisen, Z. Chkirbene, A. Mohamed, A. Erbad, and M. Guizani, “Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey,” Aug. 05 (2021), arXiv: arXiv:2108.04087. doi: 10.48550/arXiv.2108.04087.
[43] A. A. Abdellatif, N. Mhaisen, A. Mohamed, A. Erbad, and M. Guizani, “Reinforcement Learning for Intelligent Healthcare Systems: A Review of Challenges, Applications, and Open Research Issues,” IEEE Internet of Things Journal, vol. 10, no. 24, pp. 21982–22007 (Dec. 2023), doi: 10.1109/JIOT.2023.3288050.
[44] N. Ershaid, Y. Sharon, H. Doron, Y. Raz, O. Shani, N. Cohen, L. Monteran, L. Leider‑Trejo, A. Ben‑Shmuel, M. Yassin, M. Gerlic, A. Ben‑Baruch, M. Pasmanik‑Chor, R. Apte, and N. Erez, “NLRP3 inflammasome in fibroblasts links tissue damage with inflammation in breast cancer progression and metastasis,” Nature Communications, vol. 10, no. 1, pp. 4375 (Sep. 2019). doi: 10.1038/s41467‑019‑12370‑8.
[45] B. Remeseiro and V. Bolon-Canedo, “A review of feature selection methods in medical applications,” Computers in Biology and Medicine, vol. 112, pp. 103375 (Sep. 2019), doi: 10.1016/j.compbiomed.2019.103375.
[46] R. Adhao and V. Pachghare, “Feature selection using principal component analysis and genetic algorithm,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 23, no. 2, pp. 595–602 (Feb. 2020), doi: 10.1080/09720529.2020.1729507.
[47] Z. Tao, L. Huiling, W. Wenwen, and Y. Xia, “GA-SVM based feature selection and parameter optimization in hospitalization expense modeling,” Applied Soft Computing, vol. 75, pp. 323–332 (Feb. 2019), doi: 10.1016/j.asoc.2018.11.001.
[48] E. Srividhya, V. R. Niveditha, C. Nalini, K. Sinduja, S. Geeitha, P. Kirubanantham, and S. Bharati, “Integrating lncRNA gene signature and risk score to predict recurrence cervical cancer using recurrent neural network,” Measurement: Sensors, vol. 27, pp. 100782 (2023).
[49] S. Katoch, S. S. Chauhan, and V. Kumar, “A review on genetic algorithm: past, present, and future,” Multimed Tools Appl, vol. 80, no. 5, pp. 8091–8126 (Feb. 2021), doi: 10.1007/s11042-020-10139-6.
[50] I. Sangaiah and A. Vincent Antony Kumar, “Improving medical diagnosis performance using hybrid feature selection via relieff and entropy based genetic search (RF-EGA) approach: application to breast cancer prediction,” Cluster Comput, vol. 22, no. 3, pp. 6899–6906 (May 2019), doi: 10.1007/s10586-018-1702-5.
[51] T. A. Shaikh, T. Rasool, and P. Verma, “Machine intelligence and medical cyber-physical system architectures for smart healthcare: Taxonomy, challenges, opportunities, and possible solutions,” Artificial Intelligence in Medicine, vol. 146, pp. 102692 (Dec. 2023), doi: 10.1016/j.artmed.2023.102692.
[52] Y. Ao, H. Li, L. Zhu, S. Ali, and Z. Yang, “The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling,” Journal of Petroleum Science and Engineering, vol. 174, pp. 776–789 (Mar. 2019), doi: 10.1016/j.petrol.2018.11.067.
[53] B. Ma, F. Meng, G. Yan, H. Yan, B. Chai, and F. Song, “Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data,” Computers in biology and medicine, vol. 121, pp. 103761 (2020).
[54] H. Xu, Y. Zhang, L. Li, and W. Li, “Cooperative driving at unsignalized intersections using tree search,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 11, pp. 4563–4571 (2019).
[55] Y. Huang, C. Xu, M. Ji, W. Xiang, and D. He, “Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method,” BMC Med Inform Decis Mak, vol. 20, no. 1, pp. 237 (Sep. 2020), doi: 10.1186/s12911-020-01256-1.
[56] I. D. Mienye, T. G. Swart, and G. Obaido, “Recurrent neural networks: A comprehensive review of architectures, variants, and applications,” Information, vol. 15, no. 9, pp. 517 (2024).
[57] S. Kaushik, A. Choudhury, P. K. Sheron, N. Dasgupta, S. Natarajan, L. A. Pickett, and V. Dutt, “AI in Healthcare: Time‑Series Forecasting Using Statistical, Neural, and Ensemble Architectures,” Frontiers in Big Data, vol. 3, 2020, Art. no. 4, Mar. 19 (2020). doi: 10.3389/fdata.2020.00004.
[58] F. Valdez, “Swarm Intelligence: A Review of Optimization Algorithms Based on Animal Behavior,” in Recent Advances of Hybrid Intelligent Systems Based on Soft Computing, P. Melin, O. Castillo, and J. Kacprzyk, Eds., Cham: Springer International Publishing, pp. 273–298 (2021). doi: 10.1007/978-3-030-58728-4_16.
[59] S. A. Rahman, K. Obaideen, M. AlShabi, and N. Al-Yateem, “Enhancing Mental Health Care with the Kalman Filter: Predictions, Monitoring, and Personalization,” in 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 1872–1876 (Jul. 2024). doi: 10.1109/COMPSAC61105.2024.00297.
[60] H. Gunduz, “An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination,” Financ Innov, vol. 7, no. 1, pp. 28 (Apr. 2021), doi: 10.1186/s40854-021-00243-3.
[61] S. Fatima, A. Hussain, S. B. Amir, S. H. Ahmed, and S. M. H. Aslam, “XGBoost and random forest algorithms: an in depth analysis,” Pakistan Journal of Scientific Research, vol. 3, no. 1, pp. 26–31 (2023).
[62] S. A. Rahman, K. Obaideen, M. AlShabi, and N. Al-Yateem, “Enhancing Mental Health Care with the Kalman Filter: Predictions, Monitoring, and Personalization,” in 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), IEEE, 2024, pp. 1872–1876. Accessed: Feb. 28 (2025). [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10633400/
[63] A. Ala, V. Simic, D. Pamucar, and E. B. Tirkolaee, “Appointment Scheduling Problem under Fairness Policy in Healthcare Services: Fuzzy Ant Lion Optimizer,” Expert Systems with Applications, vol. 207, pp. 117949 (Nov. 2022), doi: 10.1016/j.eswa.2022.117949.

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