A Comprehensive Analysis of Big Data Predictive Analytics and IoT-Enabled Architectures across Multi-Disciplinary Domains: From Smart Healthcare to Precision Agriculture

Prerna Sterling

Authors

Prerna Sterling Department of Data Science and Systems Engineering, University of Melbourne, Australia

Keywords:

Big Data Analytics, Internet of Things, Predictive Modeling, Smart Healthcare

Abstract

This research provides an extensive exploration of the convergence between Big Data analytics, the Internet of Things (IoT), and predictive modeling within the contemporary digital landscape. As data generation reaches unprecedented scales, the necessity for robust frameworks that can process, analyze, and derive actionable insights has become a critical focal point for both academia and industry. This article systematically examines the integration of IoT-based cloud systems in smart healthcare, the role of predictive analytics in smart transportation, and the application of machine learning in precision agriculture and financial forecasting. By synthesizing diverse methodologies-ranging from Support Vector Machines and Neural Networks to Ensemble-based systems-this study delineates the theoretical and practical boundaries of current analytical paradigms. The research further addresses the ethical implications of smart city frameworks and the challenges of spam detection in social media through textual feature learning. Through a rigorous review of existing literature and the proposal of a unified analytical framework, this paper highlights the transformative potential of Big Data in enhancing clinical decision support, optimizing retail strategies, and refining electoral forecasting. The findings suggest that while technical advancements in algorithmic efficiency are significant, the future of the field lies in the interpretability of machine learning models and the ethical governance of data-driven infrastructures.

References

Awotunde JB, Jimoh RG, Ogundokun RO, Misra S, Abikoye OC (2022) Big data analytics of iot-based cloud system framework: Smart healthcare monitoring systems. In Artificial intelligence for cloud and edge computing. Springer International Publishing, Cham, pp 181–208

Babar M, Tariq MU, Alshehri MD, Ullah F, Uddin MI (2022) Smart teledentistry healthcare architecture for medical big data analysis using IoT-enabled environment. Sustainable Computing: Informatics and Systems 35:100719.

Balbin PPF, Barker JCR, Leung CK, Tran M, Wall RP, Cuzzocrea A (2020) Predictive analytics on open big data for supporting smart transportation services. Procedia Computer Science 176:3009–3018.

Balduini M, Bozzon A, Valle ED, Huang Y, Houben G (2014) Recommending Venues Using Continuous Predictive Social Media Analytics. IEEE Internet Comput 18(5):28–35.

Banumathi S, Aloysius A (2017) Predictive analytics concepts in big data- a survey. Int J Adv Res Computer Sci, Big Data, Predictive Analytics, Big Data Applications, Predictive Approaches, Challenges 8(8):4.

Bazzaz Abkenar S, Haghi Kashani M, Mahdipour E, Jameii SM (2021) Big data analytics meets social media: A systematic review of techniques, open issues, and future directions. Telematics Informatics 57:101517.

Bazzaz Abkenar S, Haghi Kashani M, Akbari M, Mahdipour E (2023) Learning textual features for Twitter spam detection: A systematic literature review. Expert Syst Appl 228:120366.

Belle A, Thiagarajan R, Soroushmehr SMR, Navidi F, Beard DA, Najarian K (2015) Big Data Analytics in Healthcare. Biomed Res Int 2015:16.

Bendre MR, Thool RC, Thool VR (2015) Big data in precision agriculture: Weather forecasting for future farming. In 2015 1st International Conference on Next Generation Computing Technologies (NGCT), 4–5 Sept. 2015 2015, pp. 744–750,

Bendre MR, Thool RC, Thool VR (2016) Big Data in Precision Agriculture Through ICT: Rainfall Prediction Using Neural Network Approach. In: Satapathy S, Bhatt Y, Joshi A, Mishra D (eds) Proceedings of the International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 438. Springer, Singapore.

Bhuimali A, Aithal S, Paul PK (2018) Business Informatics: With Special Reference To Big Data As An Emerging Area: A Basic Review. Int J Recent Researches in Science, Eng Technol, vol. 6,

Bianchi DG (2019) Politics and big data. Nowcasting and forecasting elections with social media. Contemporary Italian Politics, pp. 1–2, 2019,

Biesialska K, Franch X, Muntés-Mulero V (2021) Big Data analytics in Agile software development: A systematic mapping study. Inf Software Technol 132:106448.

Bradlow ET, Gangwar M, Kopalle P, Voleti S (2017) The Role of Big Data and Predictive Analytics in Retailing. J Retail 93(1):79–95.

Brereton P, Kitchenham BA, Budgen D, Turner M, Khalil M (2007) Lessons from applying the systematic literature review process within the software engineering domain. J Syst Software 80(4):571–583.

Breur T (2016) Statistical Power Analysis and the contemporary “crisis” in social sciences. J Marketing Analytics 4(2):61–65.

Casado R, Younas M (2015) Emerging trends and technologies in big data processing. Concurrency and Computation: Practice and Experience 27(8):2078–2091.

Chang V (2021) An ethical framework for big data and smart cities. Technol Forecast Soc Chang 165:120559.

V Dhar, 2001, “Predictions in Financial Markets: The Case of Small Disjuncts”, ACM Transaction on Intelligent Systems and Technology, Vol-2, Issue-3.

J Osheroff, J Teich, B Middleton, E Steen, A Wright, D Detmer, 2007, “A Roadmap for National Action on Clinical Decision Support”, JAMIA: A Scholarly Journal of Informatics in Health and Biomedicine, Vol-14, Issue-2, Pages-141-145.

B Kaminski, M Jakubczyk, P Szufel, 2018, “A framework for sensitivity analysis of decision trees”, Central European Journal of Operations Research, Vol-26, Issue-1, Pages-135-159.

J S Armstrong, 2012, “Illusions in regression analysis”, International Journal of Forecasting, Vol-28, Issue-3, Pages-689-694.

W S McCulloch, Walter Pitts, 1943, “A logical calculus of the ideas immanent in nervous activities”, The bulletin of mathematical biophysics, Vol-5, Issue-4, Pages-115-133.

Peter M Lee, 2012, “Bayesian Statistics: An Introduction, 4th Edition”, John Willey and Sons Ltd.

R Polikar, 2006, “Ensemble based Systems in decision making”, IEEE Circuits and Systems Magazine, Vol-6, Issue-3, Pages-21-45.

J H Friedman, 1999, “Greedy Function Approximation: A Gradient Boosting Machine”, Lecture notes.

C Cortes, 1995, “Support-vector networks”, Machine Learning, Vol-20, Issue-3, Pages- 273-297.

Ben Hur et al, 2001, “Support Vector Clustering”, Journal of Machine Learning Research, Vol-2, Pages-125-137.

Norvig PR, Intelligence SA. A modern approach. Prentice Hall Upper Saddle River, NJ, USA; 2002.

Rani M, Nayak R, Vyas OP. An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage. Knowledge-Based Systems. 2015;90:33–48.

Sutton RS, Barto AG. Reinforcement learning: An introduction. Vol. 1, No. 1. Cambridge: MIT Press; 1998. p. 9–11.

Deng Y, Bao F, Kong Y, Ren Z, Dai Q. Deep direct reinforcement learning for financial signal representation and trading. IEEE Trans Neural Netw Learn Syst. 2016;28(3):653–664.

Rudin C, Chen C, Chen Z, Huang H, Semenova L, Zhong C. Interpretable machine learning: Fundamental principles and 10 grand challenges. Stat Surv. 2022;16:1–85.

Buckner C. Understanding adversarial examples requires a theory of artefacts for deep learning. Nat Mach Intell. 2020;2(12):731–736.

A. K. Bhat and G. Krishnan, "A Review of Artificial Intelligence in Finance: Driving the Next Wave of Industry Innovation," 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), Coimbatore, India, 2025, pp. 2033-2040, doi: 10.1109/ICoICI65217.2025.11252894.