Optimized Computational Methods for Disease Prediction Using Machine Learning

Authors

  • Misbha Taj, Sasikala Author

DOI:

https://doi.org/10.48047/cfehf924

Keywords:

Disease Prediction, XGBoost, Deep Neural Network, LR, Random Forest

Abstract

Accurate disease prediction is crucial for early diagnosis and effective treatment planning. Traditional diagnostic methods often rely on manual examination, which can be time-consuming and prone to errors. This paper presents a comparative analysis of different ML algorithms used for disease prediction and explores optimization techniques such as feature selection, hyperparameter tuning, and ensemble learning. Our experimental results demonstrate the effectiveness of optimized ML models in improving prediction accuracy and reducing computational complexity.   

Downloads

Download data is not yet available.

References

applications, Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. Review of deep learning: concepts, CNN architectures, challenges,

future directions. https://doi.org/10.1186/s40537-021-00444-8.

J Big Data 8, 53 (2021). Yamashita, R., Nishio, M., Do, R.K.G. et al. Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629 (2018). https://doi.org/10.1007/s13244018-0639-9.

Downloads

Published

2025-04-03

How to Cite

Misbha Taj, Sasikala. (2025). Optimized Computational Methods for Disease Prediction Using Machine Learning . Cuestiones De Fisioterapia, 54(5), 520-524. https://doi.org/10.48047/cfehf924