Optimized Computational Methods for Disease Prediction Using Machine Learning
DOI:
https://doi.org/10.48047/cfehf924Keywords:
Disease Prediction, XGBoost, Deep Neural Network, LR, Random ForestAbstract
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.
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