Abstract

The present paper focuses on the analysis of healthcare data using XGBoost algorithm at the Fog Layer. The present paper is based on an implementation of the Edge-Fog Layer layered architecture by considering the ESP8266 as the Edge node and Raspberry Pi is the Fog Node. The data has been captured, encrypted based on a custom-built encryption algorithm named SPADE and forwarded to the Raspberry Pi using the MQTT publish subscribe model. The Raspberry Pi acts as the data aggregator from multiple other Edge Nodes. The data within Raspberry Pi then analyzed based on the XGBoost algorithm after decryption which is done using Reverse SPADE algorithm. The results of analysis are then communicated to relevant application programs using FastAPI while also being stored on the Firebase database. The present framework provides a cost-effective implementation mechanism for analysis of healthcare data received from Edge nodes in a secure manner. The results of the analysis are presented in the form of prediction whether the health condition of the patient is critical. This results in the healthcare providers being able to initiate necessary healthcare procedures required to improve the healthcare condition of the patient.

References

• Palli, G. H., Mirza, G. F., & Chowdhry, B. S. (2022). Novel IoT-Based E-Health System: Hospital Management, Telemedicine and Quarantine Management for COVID-19. Proceedings of 3rd International Conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2022. https://doi.org/10.1109/INTELLECT55495.2022.9969396
• Samal, S., & Mitra, T. (2023). IoT-Based E-Health Monitoring System for Pre-Schoolers. In Applications of Artificial Intelligence in the Healthcare Sector.
• Dubey, H., Monteiro, A., Constant, N., Abtahi, M., Borthakur, D., Mahler, L., Sun, Y., Yang, Q., Akbar, U., & Mankodiya, K. (2017). Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications. https://doi.org/10.1007/978-3-319-58280- 1_11
• Selvarathi, C., & Sujatha, R. (2018). Frequent updater for health monitoring system using Raspberry Pi. International Journal of Engineering and Technology (UAE), 7(3.34 Special Issue (34). https://doi.org/10.14419/ijet.v7i3.34.18975
• Keserwani, H., Kakade, S. v., Sharma, S. K., Manchanda, M., & Nama, G. F. (2023). Real-Time Analysis of Wearable Sensor Data Using IoT and Machine Learning in Healthcare. International Journal of Intelligent Systems and Applications in Engineering, 11(7s).
• Hatem, A. S., Abed, J. K., & Ajel, A. R. (2023). Based on IoT: Design and implementation of health care monitoring system. AIP Conference Proceedings, 2804(1). https://doi.org/10.1063/5.0155061
• Panganiban, Edward & Chung, Wen-Yaw & Tai, Wei- Chieh & Paglinawan, Arnold & Lai, Jheng-Siang & Cheng, Ren & Chang, Ming-Kai & Chang, Po-Hsuan. (2020). Real-Time Intelligent Healthcare Monitoring and Diagnosis System Through Deep Learning and Segmented Analysis. 10.1007/978-3-030-30636-6_3.
• Upreti, K., Vats, P., Haque, M., Goyal, A. A., Wankhede, S. R., Singh, P., Alam, M. S., & Nasir, M. S. (2023). IoT-Based Control System to Measure, Analyze, and Track Basic Vital Indicators in Patient Healthcare Monitoring System. Communications in Computer and Information Science, 1749 CCIS. https://doi.org/10.1007/978-3-031- 25088-0_63
• Hatem, A. S., Abed, J. K., & Ajel, A. R. (2023). Based on IoT: Design and implementation of health care monitoring system. AIP Conference Proceedings, 2804(1). https://doi.org/10.1063/5.0155061
• [10] Keserwani, H., Kakade, S. v., Sharma, S. K., Manchanda, M., & Nama, G. F. (2023). Real-Time Analysis of Wearable Sensor Data Using IoT and Machine Learning in Healthcare. International Journal of Intelligent Systems and Applications in Engineering, 11(7s).
• Mhamdi, L., Dammak, O., Cottin, F., & Dhaou, I. Ben. (2022). Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems. Biomedicines, 10(8). https://doi.org/10.3390/biomedicines10082013
• Lakshmi, G. J., Ghonge, M., & Obaid, A. J. (2021). Cloud based iot smart healthcare system for remote patient monitoring. EAI Endorsed Transactions on Pervasive Health and Technology, 7(28). https://doi.org/10.4108/eai.15-7-2021.170296
• Gupta, M. S. D., Patchava, V., & Menezes, V. (2016). Healthcare based on IoT using Raspberry Pi. Proceedings of the 2015 International Conference on Green Computing and Internet of Things, ICGCIoT 2015. https://doi.org/10.1109/ICGCIoT.2015.7380571
• Prodhan, U. K., Saha, T. K., Shaharin, R., Emon, T. A., & Rahman, M. Z. (2020). Implementation of Low-Cost Remote Primary Healthcare Services through Telemedicine: Bangladesh Perspectives. International Journal of Advanced Computer Science and Applications, 11(11). https://doi.org/10.14569/IJACSA.2020.0111118
• Segarra, C., Delgado-Gonzalo, R., & Schiavoni, V. (2020). MQT-Tz: Secure MQTT broker for biomedical signal processing on the edge. Studies in Health Technology and Informatics, 270. https://doi.org/10.3233/SHTI200177
• Nambiar, A., Harikrishnaa, S., & Sharanprasath, S. (2023). Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1272506
• Gupta, P., Maji, S., & Mehra, R. (2022). Predictive Modeling of Stress in the Healthcare Industry during COVID-19: A Novel Approach Using XGBoost, SHAP Values, and Tree Explainer. International Journal of Decision Support System Technology, 15(1). https://doi.org/10.4018/IJDSST.315758
• Sharifi-Kia, A., Nahvijou, A., & Sheikhtaheri, A. (2023). Machine learning-based mortality prediction models for smoker COVID-19 patients. BMC Medical Informatics and Decision Making, 23(1). https://doi.org/10.1186/s12911- 023-02237-w
• Nagassou, M., Mwangi, R. W., & Nyarige, E. (2023). A Hybrid Ensemble Learning Approach Utilizing Light Gradient Boosting Machine and Category Boosting Model for Lifestyle-Based Prediction of Type-II Diabetes Mellitus. Journal of Data Analysis and Information Processing, 11(04). https://doi.org/10.4236/jdaip.2023.114025
• Tiwari, P., & Singh, V. (2021). Diabetes disease prediction using significant attribute selection and classification approach. Journal of Physics: Conference Series, 1714(1). https://doi.org/10.1088/1742-6596/1714/1/012013
• Modak, S. K. S., & Jha, V. K. (2024). Diabetes prediction model using machine learning techniques. Multimedia Tools and Applications, 83(13). https://doi.org/10.1007/s11042-023-16745-4
• Ahamed, B. S., Arya, M. S., & Nancy V, A. O. (2022). Prediction of Type-2 Diabetes Mellitus Disease Using Machine Learning Classifiers and Techniques. In Frontiers in Computer Science (Vol. 4). https://doi.org/10.3389/fcomp.2022.835242
• Lu, H., Uddin, S., Hajati, F., Moni, M. A., & Khushi, M. (2022). A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus. Applied Intelligence, 52(3). https://doi.org/10.1007/s10489-021-02533-w
• Ou, S. M., Tsai, M. T., Lee, K. H., Tseng, W. C., Yang, C. Y., Chen, T. H., Bin, P. J., Chen, T. J., Lin, Y. P., Sheu, W.H. H., Chu, Y. C., & Tarng, D. C. (2023). Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms. BioData Mining, 16(1). https://doi.org/10.1186/s13040-023-00324-2
• Chen, Y., Yang, T., Gao, X., & Xu, A. (2022). Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis. Frontiers of Medicine, 16(3). https://doi.org/10.1007/s11684-021-0828-7
• Huang, A. A., & Huang, S. Y. (2023b). Use of machine learning to identify risk factors for insomnia. PLoS ONE, 18(4 April). https://doi.org/10.1371/journal.pone.0282622
• Yang, J. C. (2024). The prediction and analysis of heart disease using XGBoost algorithm. Applied and Computational Engineering, 41(1). https://doi.org/10.54254/2755-2721/41/20230711
• Ganie, S. M., Pramanik, P. K. D., Malik, M. B., Nayyar, A., & Kwak, K. S. (2023). An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms. Computer Systems Science and Engineering, 46(3). https://doi.org/10.32604/csse.2023.035244
• Shah, A., Ahirrao, S., Pandya, S., Kotecha, K., & Rathod, S. (2021). Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.762303
• Zhang, L., Zhou, X., & Cao, J. (2024). Establishment and validation of a heart failure risk prediction model for elderly patients after coronary rotational atherectomy based on machine learning. PeerJ, 12. https://doi.org/10.7717/peerj.16867
• Kumar, D., Sood, S. K., & Rawat, K. S. (2023). Early health prediction framework using XGBoost ensemble algorithm in intelligent environment. Artificial Intelligence Review, 56. https://doi.org/10.1007/s10462-023-10565-6
• Li, Y., Cao, W., Chang, W., Zhou, S., & Zhang, R. (2022). An XGBoost risk prediction model of cardiovascular and cerebrovascular diseases with plateau healthcare dataset. ACM International Conference Proceeding Series. https://doi.org/10.1145/3573834.3574558
• Sharma, A., & Verbeke, W. J. M. I. (2020). Improving Diagnosis of Depression with XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n=11,081). Frontiers in Big Data, 3. https://doi.org/10.3389/fdata.2020.00015
• Kumar, M., Singhal, S., Shekhar, S., Sharma, B., & Srivastava, G. (2022). Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning. Sustainability (Switzerland), 14(21). https://doi.org/10.3390/su142113998
• Palkar, A., Dias, C. C., Chadaga, K., & Sampathila, N. (2024). Empowering Glioma Prognosis with Transparent Machine Learning and Interpretative Insights Using Explainable AI. IEEE Access, 12. https://doi.org/10.1109/ACCESS.2024.3370238
• Khanna, V. V., Chadaga, K., Sampathila, N., Prabhu, S., & Rajagopala Chadaga, P. (2023). A machine learning and explainable artificial intelligence triage-prediction system for COVID-19. Decision Analytics Journal, 7.
• Rocha, C. N., & Rodrigues, F. (2021). Forecasting emergency department admissions. Journal of Intelligent Information Systems, 56(3). https://doi.org/10.1007/s10844-021-00638-9
• Coppa, K., Kim, E. J., Oppenheim, M. I., Bock, K. R., Zanos, T. P., & Hirsch, J. S. (2023). Application of a Machine Learning Algorithm to Develop and Validate a Prediction Model for Ambulatory Non-Arrivals. Journal of General Internal Medicine, 38(10). https://doi.org/10.1007/s11606-023-08065-y
• Aldhoayan, M. D., & Alobaidi, R. M. (2023). The Use of Machine Learning to Predict Late Arrivals at the Adult Outpatient Department. Cureus. https://doi.org/10.7759/cureus.39886
• Alabbad, D. A., Almuhaideb, A. M., Alsunaidi, S. J., Alqudaihi, K. S., Alamoudi, F. A., Alhobaishi, M. K., Alaqeel, N. A., & Alshahrani, M. S. (2022). Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia. Informatics in Medicine Unlocked, 30. https://doi.org/10.1016/j.imu.2022.100937
• Parthasarathy, S., Lakshminarayanan, A. R., Khan, A. A. A., Sathick, K. J., & Jayaraman, V. (2023). Detection of Health Insurance Fraud using Bayesian Optimized XGBoost. International Journal of Safety and Security Engineering, 13(5). https://doi.org/10.18280/ijsse.130509
• Alle, S., Karthikeyan, A., Kanakan, A., Siddiqui, S., Garg, A., Mehta, P., Mishra, N., Chattopadhyay, P., Devi, P., Waghdhare, S., Tyagi, A., Tarai, B., Hazarik, P. P., Das, P., Budhiraja, S., Nangia, V., Dewan, A., Sethuraman, R., Subramanian, C., … Priyakumar, U. D. (2022). COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits. PLoS ONE, 17(3 March). https://doi.org/10.1371/journal.pone.0264785
• Hammoud, B., Semaan, A., Elhajj, I., & Benova, L. (2022). Can machine learning models predict maternal and newborn healthcare providers’ perception of safety during the COVID-19 pandemic? A cross-sectional study of a global online survey. Human Resources for Health, 20(1). https://doi.org/10.1186/s12960-022-00758-5
• Ghosh, S., Das, J., Ghosh, S. K., & Buyya, R. (2020). CLAWER: Context-aware Cloud-Fog based Workflow Management Framework for Health Emergency Services. Proceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020. https://doi.org/10.1109/CCGrid49817.2020.000-5
• Barak-Corren, Y., Chaudhari, P., Perniciaro, J., Waltzman, M., Fine, A. M., & Reis, B. Y. (2021). Prediction across healthcare settings: a case study in predicting emergency department disposition. Npj Digital Medicine, 4(1). https://doi.org/10.1038/s41746-021-00537-x
• Chauhan, R., Goel, A., Alankar, B., & Kaur, H. (2024). Predictive modeling and web-based tool for cervical cancer risk assessment: A comparative study of machine learning models. MethodsX, 12. https://doi.org/10.1016/j.mex.2024.102653
• Finkelstein, J., Cui, W., Martin, T. C., & Parsons, R. (2022). Machine Learning Approaches for Early Prostate Cancer Prediction Based on Healthcare Utilization Patterns. Studies in Health Technology and Informatics, 289. https://doi.org/10.3233/SHTI210860 Eledkawy, A., Hamza, T., & El-Metwally, S. (2024). Precision cancer classification using liquid biopsy and advanced machine learning techniques. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-56419-1
• Huang, A. A., & Huang, S. Y. (2023b). Use of machine learning to identify risk factors for insomnia. PLoS ONE, 18(4 April). https://doi.org/10.1371/journal.pone.0282622
• Rahmani, K., Garikipati, A., Barnes, G., Hoffman, J., Calvert, J., Mao, Q., & Das, R. (2022). Early prediction of central line associated bloodstream infection using machine learning. American Journal of Infection Control, 50(4). https://doi.org/10.1016/j.ajic.2021.08.017
• Hui, M., Ma, J., Yang, H., Gao, B., Wang, F., Wang, J., Lv, J., Zhang, L., Yang, L., & Zhao, M. (2023). ESKD Risk Prediction Model in a Multicenter Chronic Kidney Disease Cohort in China: A Derivation, Validation, and Comparison Study. Journal of Clinical Medicine, 12(4). https://doi.org/10.3390/jcm12041504
• Gupta, A., & Singh, A. (2023). Prediction Framework on Early Urine Infection in IoT–Fog Environment Using XGBoost Ensemble Model. Wireless Personal Communications, 131(2). https://doi.org/10.1007/s11277-023-10466-5
• Wong, G. L. H., Yuen, P. C., Ma, A. J., Chan, A. W. H., Leung, H. H. W., & Wong, V. W. S. (2021). Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. Journal of Gastroenterology and Hepatology (Australia), 36(3). https://doi.org/10.1111/jgh.15385
• Ghandian, S., Thapa, R., Garikipati, A., Barnes, G., Green- Saxena, A., Calvert, J., Mao, Q., & Das, R. (2022). Machine learning to predict progression of non-alcoholic fatty liver to non-alcoholic steatohepatitis or fibrosis. JGH Open, 6(3). https://doi.org/10.1002/jgh3.12716
• Pang, X., Forrest, C. B., Lê-Scherban, F., & Masino, A. J. (2021). Prediction of early childhood obesity with machine learning and electronic health record data. International Journal of Medical Informatics, 150. https://doi.org/10.1016/j.ijmedinf.2021.104454
• Lipesa, B. A., Okango, E., Omolo, B. O., & Omondi, E. O. (2023). An application of a supervised machine learning model for predicting life expectancy. SN Applied Sciences, 5(7). https://doi.org/10.1007/s42452-023-05404-w
• Sun, W., Kalmady, S. V., Sepehrvand, N., Salimi, A., Nademi, Y., Bainey, K., Ezekowitz, J. A., Greiner, R., Hindle, A., McAlister, F. A., Sandhu, R. K., & Kaul, P. (2023). Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms. Npj Digital Medicine, 6(1). https://doi.org/10.1038/s41746-023-00765-3
• [52] Wong, G. L. H., Yuen, P. C., Ma, A. J., Chan, A. W. H.,Leung, H. H. W., & Wong, V. W. S. (2021). Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. Journal of Gastroenterology and Hepatology (Australia), 36(3). https://doi.org/10.1111/jgh.15385
• Yang, J. C. (2024). The prediction and analysis of heart disease using XGBoost algorithm. Applied and Computational Engineering, 41(1). https://doi.org/10.54254/2755-2721/41/20230711
• Zhang, L., Zhou, X., & Cao, J. (2024). Establishment and validation of a heart failure risk prediction model for elderly patients after coronary rotational atherectomy based on machine learning. PeerJ, 12. https://doi.org/10.7717/peerj.16867
• Segarra, C., Delgado-Gonzalo, R., & Schiavoni, V. (2020). MQT-Tz: Secure MQTT broker for biomedical signal processing on the edge. Studies in Health Technology and Informatics, 270. https://doi.org/10.3233/SHTI200177
• Palkar, A., Dias, C. C., Chadaga, K., & Sampathila, N. (2024). Empowering Glioma Prognosis With Transparent Machine Learning and Interpretative Insights Using Explainable AI. IEEE Access, 12. https://doi.org/10.1109/ACCESS.2024.3370238.
• Rocha, C. N., & Rodrigues, F. (2021). Forecasting emergency department admissions. Journal of Intelligent Information Systems, 56(3). https://doi.org/10.1007/s10844-021-00638-9