Volume : 9, Issue : 2, FEB 2023

MACHINE LEARNING IN HEALTHCARE: NAVIGATING THE COMPLEXITIES AND OPPORTUNITIES

YASHVANTH M

Abstract

Machine learning (ML) has been widely applied in healthcare to improve the accuracy and efficiency of diagnosis, treatment, and prevention of diseases. In this review article, we will explore the current state of ML in healthcare by examining the different methodologies used, such as supervised learning, unsupervised learning, and deep learning. We will also discuss their applications in areas such as medical imaging, genomics, and electronic health records. Additionally, we will delve into the challenges and limitations of using ML in healthcare, such as the need for large and diverse datasets, and addressing ethical concerns. Furthermore, we will provide a comprehensive overview of the current and potential future impact of ML on healthcare, highlighting the opportunities and challenges for further research and development in this field. This review article aims to provide a clear and concise overview of the current state of ML in healthcare, including its methodologies, applications and challenges, with the objective of providing direction for future research in this field.

Article : Download PDF

Cite This Article

-

Article No : 3

Number of Downloads : 511

References

1. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

2. Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A. & Leng, T. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410.

3. Hussein, M., Alkawaz, M. A., & Al-Jumaily, A. (2018). Automated detection of Parkinson’s disease through speech analysis. Journal of Ambient Intelligence and Humanized Computing, 9(6), 4565-4575.

4. Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554

5. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

6. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G.& Pe- tersen, S. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.

7. Setio, A. A., Ciompi, F., Willemmens, K., Naeye, R., & van Rikxoort, E. M. (2016). Deep learning-based lung nodule classification using CT images. IEEE transactions on medical imaging, 35(5), 1160-1169.

8. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G. & Dieleman, S. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.

9. Wang, Z., Yan, S., Y depl, P., & Vuong, Q. H. (2017). Automated liver lesion segmen- tation and quantification in CT images using a cascaded fully convolutional network. Medical Image Analysis, 35, 40-50.

10. Yao, L., Torr, P. H., & Hospedales, T. M. (2016). Anticipating deep features for early diagnosis of Alzheimer’s disease. arXiv preprint arXiv:1605.08486.