ISSN : 2583-2646

Predicting tomorrow’s Ailments: How AI/ML Is Transforming Disease Forecasting

ESP Journal of Engineering & Technology Advancements
© 2021 by ESP JETA
Volume 1  Issue 2
Year of Publication : 2021
Authors : Mohit Surender Reddy, Manikanth Sarisa, Siddharth Konkimalla, Sanjay Ramdas Bauskar, Hemanth Kumar Gollangi, Eswar Prasad Galla, Shravan Kumar Rajaram
: 10.56472/25832646/ESP-V1I2P120

Citation:

Mohit Surender Reddy, Manikanth Sarisa, Siddharth Konkimalla, Sanjay Ramdas Bauskar, Hemanth Kumar Gollangi, Eswar Prasad Galla, Shravan Kumar Rajaram, 2021. "Predicting tomorrow’s Ailments: How AI/ML Is Transforming Disease Forecasting", ESP Journal of Engineering & Technology Advancements, 1(2): 188-200.

Abstract:

The most common of these technologies include artificial intelligence (AI) as well as machine learning (ML), both of which are revolutionizing the healthcare system, especially in disease prediction. Given the emerging data produced from systems in healthcare, EHRs, social media, environment, and genomics, AI and ML algorithms continue to find genuine applications in anticipating disease outbreaks or patients’ health futures. Disease forecasting is looked at in this paper with regard to the different methods and algorithms being used in current practice, as well as the possibility that AI/ML could do more in identifying patterns and relationships that are difficult to decipher using traditional statistical analytical tools. It also presents issues that are associated with the adoption of AI/ML in medicine for instance, data protection, prejudice in the algorithms, and the intractability of the AI/ML models. There is potential seen in the use of AI in disease prediction in that it will help in the early detection of disease outbreaks, modelling of chronic disease and progression, and development of treatment plans. These developments have already been implemented in healthcare organizations globally and have positively impacted patient satisfaction as well as the management of healthcare. However, with such prospects come steep obstacles, as there are technical and ethical constraints that need to be crossed before the full potential of AI/ML in disease prediction can be achieved. This article has the purpose of reviewing the current AI/ML developments in disease forecasting, showing how they are used in the field including epidemiology, oncology, cardiovascular diseases and rare diseases. Finally, it highlights some trends, both synergistic and adverse, the concept of ethical decision-making and other aspects of this fairly new and dynamic discipline.

References:

[1] Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014.

[2] Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. (No Title).

[3] Sharma, N., Dev, J., Mangla, M., Wadhwa, V. M., Mohanty, S. N., & Kakkar, D. (2021). A heterogeneous ensemble forecasting model for disease prediction. New Generation Computing, 1-15.

[4] Eksin, C., Paarporn, K., & Weitz, J. S. (2019). Systematic biases in disease forecasting–the role of behavior change. Epidemics, 27, 96-105.

[5] Lee, K., Ray, J., & Safta, C. (2021). The predictive skill of convolutional neural networks models for disease forecasting. Plos one, 16(7), e0254319.

[6] Yu, W., Liu, T., Valdez, R., Gwinn, M., & Khoury, M. J. (2010). Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC medical informatics and decision making, 10, 1-7.

[7] Assegie, T. A. (2021). Support vector machine and k-nearest neighbor based liver disease classification model. Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, 3(1), 9-14.

[8] Chae, S., Kwon, S., & Lee, D. (2018). Predicting infectious disease using deep learning and big data. International journal of environmental research and public health, 15(8), 1596.

[9] Meyer, H., & Salathé, M. (2015). "Data-driven modeling of infectious disease outbreaks: A systematic review." PLoS Computational Biology, 11(10), e1004545.

[10] Bansal, S., Khandelwal, S., & Jha, S. (2016). "Predicting infectious disease outbreaks: The potential of big data." International Journal of Infectious Diseases, 50, 111-113.

[11] Zhang, L., & Zhang, J. (2017). "A machine learning approach for prediction of infectious diseases." Health Informatics Journal, 23(1), 2-12.

[12] Huang, C., & Zhang, Y. (2018). "Deep learning for disease forecasting: A review." Journal of Healthcare Engineering, 2018, Article ID 7428431.

[13] Paltiel, A. D., Zheng, A., & Zheng, S. (2020). "Assessment of model-based forecasts of COVID-19 in the United States." JAMA Network Open, 3(9), e2017040.

[14] Kouadio, L., & Fagbohun, E. D. (2020). "Artificial intelligence and machine learning in healthcare: Applications and challenges." International Journal of Health Sciences, 14(3), 6-15.

[15] Aldabbas, H. (2020). "Machine learning in disease prediction: A review." Journal of Medical Systems, 44(6), 1-12.

Keywords:

Artificial Intelligence, Machine Learning, Disease Forecasting, Predictive Analytics, Epidemiology, Algorithmic Bias.