Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
|
---|---|---|
Article Number | 01013 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/e3sconf/202343001013 | |
Published online | 06 October 2023 |
Performance Comparison of ML Algorithms for Sustainable Smart Health Systems
1 ECE Department, Sreenidhi Institute of Science and Technology, Hyderabad, India.
2 ECE Department, Gokaraju Rangaraju Institute of Engineering and technology, Hyderabad, India.
3 Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, India
4 KG Reddy College of Engineering & Technology, Hyderabad, India.
* Corresponding author: kswaraja@gmail.com
Disease prognosis holds immense significance in healthcare due to its potential to greatly improve patient outcomes through early and precise diagnosis. Machine learning (ML) algorithms provide a robust avenue for disease prediction, employing patient data analysis to detect intricate patterns of specific ailments. Machine learning algorithms adeptly handle intricate and extensive datasets, uncovering latent patterns often eluding human observation. By considering diverse symptoms and their permutations, ML models yield precise forecasts concerning the probability of distinct diseases. The investigation begins by laying a basis in sustainable development concepts, recognising the need of resource optimisation, energy efficiency, and minimal environmental effect in the context of healthcare technology. Categorically, disease prediction methodologies fall under supervised and unsupervised learning categories, involving training algorithms on annotated datasets containing symptoms and corresponding diagnoses. These trained models can then anticipate diseases based on novel symptom profiles.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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