Enhancing Decision-Making in Public Health Informatics Using AI and Big Data Analytics
DOI:
https://doi.org/10.56294/mw2024512Keywords:
Artificial Intelligence, Big Data Analytics, Public Health Informatics, Predictive Models, Healthcare Decision-MakingAbstract
In public health computing, artificial intelligence (AI) and big data analytics together provide a wealth of fresh approaches to handle significant public health issues, enhance patient outcomes, and guide choices. Standard approaches of analysis may fail to provide real-time insights that can be utilised to move fast as the volume of data in healthcare systems all across the globe rises. Together, artificial intelligence (AI) and big data analytics can manage enormous volumes of various kinds of health data, including social aspects of health, public health data, and electronic health records (EHR). This combination allows one to build prediction models able to detect emerging illnesses, see health trends approaching, and identify groups of persons at risk. From vast volumes of data, artificial intelligence systems—including deep learning and machine learning—can identify helpful patterns. This clarifies risk factors, forecasts disease outbreaks, and guides choices on the most efficient use of resources. Moreover, Big Data analytics allows us to examine large-scale effects of activities, thereby enabling individuals in decision-making to do so grounded on strong evidence. By anticipating how each patient will do, thus improving treatments, and so reducing variations in access to and outcomes of healthcare, using AI and Big Data combined may also assist to personalise healthcare. Using AI and Big Data in public health informatics presents some challenges even with these advances. Concerns concerning data security, the requirement of uniform data formats, and the possibility that algorithms may produce biassed choices abound, for instance. Dealing with these problems is very crucial if we are to guarantee fair and ethical use of Big Data and artificial intelligence to enhance public health choices. This article discusses how Big Data analytics and artificial intelligence will transform public health informatics going forward. It lists their advantages and drawbacks and offers ideas for improving the responses on the pitch.
References
Carra, G.; Salluh, J.I.; Ramos, F.J.d.S.; Meyfroidt, G. Data-driven ICU management: Using Big Data and algorithms to improve outcomes. J. Crit. Care 2020, 60, 300–304. DOI: https://doi.org/10.1016/j.jcrc.2020.09.002
Gupta, N.S.; Kumar, P. Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine. Comput. Biol. Med. 2023, 162, 107051. DOI: https://doi.org/10.1016/j.compbiomed.2023.107051
McPadden, J.; Durant, T.J.; Bunch, D.R.; Coppi, A.; Price, N.; Rodgerson, K.; Torre, C.J., Jr.; Byron, W.; Hsiao, A.L.; Krumholz, H.M.; et al. Health care and precision medicine research: Analysis of a scalable data science platform. J. Med. Internet Res. 2019, 21, e13043. DOI: https://doi.org/10.2196/13043
Muhammed Juman.B K, J Christoher. (2015). A Study on Direct Selling Business (Amway India Ltd) in Kerala: A Case Study of Calicut District of Kerala. International Journal on Research and Development - A Management Review, 4(4), 23 - 30.
Swain, D.; Mehta, U.; Bhatt, A.; Patel, H.; Patel, K.; Mehta, D.; Acharya, B.; Gerogiannis, V.C.; Kanavos, A.; Manika, S. A Robust Chronic Kidney Disease Classifier Using Machine Learning. Electronics 2023, 12, 212. DOI: https://doi.org/10.3390/electronics12010212
Nusir, M.; Louati, A.; Louati, H.; Tariq, U.; Abu Zitar, R.; Abualigah, L.; Gandomi, A.H. Design Research Insights on Text Mining Analysis: Establishing the Most Used and Trends in Keywords of Design Research Journals. Electronics 2022, 11, 3930. DOI: https://doi.org/10.3390/electronics11233930
AlZu’bi, S.; Abu Zitar, R.; Hawashin, B.; Abu Shanab, S.; Zraiqat, A.; Mughaid, A.; Almotairi, K.H.; Abualigah, L. A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education. Electronics 2022, 11, 2964. DOI: https://doi.org/10.3390/electronics11182964
Ai, M.A.S.; Shanmugam, A.; Muthusamy, S.; Viswanathan, C.; Panchal, H.; Krishnamoorthy, M.; Elminaam, D.S.A.; Orban, R. Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network. Electronics 2022, 11, 2250. DOI: https://doi.org/10.3390/electronics11142250
Daradkeh, M.; Abualigah, L.; Atalla, S.; Mansoor, W. Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics. Electronics 2022, 11, 2066 DOI: https://doi.org/10.3390/electronics11132066
Fathimathul Rajeena, P.P.; Orban, R.; Vadivel, K.S.; Subramanian, M.; Muthusamy, S.; Elminaam, D.S.A.; Nabil, A.; Abualigah, L.; Ahmadi, M.; Ali, M.A. A novel method for the classification of butterfly species using pre-trained CNN models. Electronics 2022, 11, 2016. DOI: https://doi.org/10.3390/electronics11132016
Ali, M.A.S.; Balasubramanian, K.; Krishnamoorthy, G.D.; Muthusamy, S.; Pandiyan, S.; Panchal, H.; Mann, S.; Thangaraj, K.; El-Attar, N.E.; Abualigah, L.; et al. Classification of Glaucoma Based on Elephant-Herding Optimization Algorithm and Deep Belief Network. Electronics 2022, 11, 1763. DOI: https://doi.org/10.3390/electronics11111763
Alfaiz, N.S.; Fati, S.M. Enhanced Credit Card Fraud Detection Model Using Machine Learning. Electronics 2022, 11, 662. DOI: https://doi.org/10.3390/electronics11040662
Daradkeh, M.K. A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction. Electronics 2022, 11, 250. DOI: https://doi.org/10.3390/electronics11020250
Nadimi-Shahraki, M.H.; Mohammadi, S.; Zamani, H.; Gandomi, M.; Gandomi, A.H. A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis. Electronics 2021, 10, 3167. DOI: https://doi.org/10.3390/electronics10243167
Nadimi-Shahraki, M.H.; Taghian, S.; Mirjalili, S.; Abualigah, L.; Elaziz, M.A.; Oliva, D. EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem. Electronics 2021, 10, 2975. DOI: https://doi.org/10.3390/electronics10232975
Hussien, A.G.; Abualigah, L.; Abu Zitar, R.; Hashim, F.A.; Amin, M.; Saber, A.; Almotairi, K.H.; Gandomi, A.H. Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications. Electronics 2022, 11, 1919. DOI: https://doi.org/10.3390/electronics11121919
Mir, I.; Gul, F.; Mir, S.; Khan, M.A.; Saeed, N.; Abualigah, L.; Abuhaija, B.; Gandomi, A.H. A Survey of Trajectory Planning Techniques for Autonomous Systems. Electronics 2022, 11, 2801. DOI: https://doi.org/10.3390/electronics11182801
Singh, M.; Pujar, G.V.; Kumar, S.A.; Bhagyalalitha, M.; Akshatha, H.S.; Abuhaija, B.; Alsoud, A.R.; Abualigah, L.; Beeraka, N.M.; Gandomi, A.H. Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications. Electronics 2022, 11, 2634. DOI: https://doi.org/10.3390/electronics11172634
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