Patients’ Health Analysis using Machine Learning
DOI:
https://doi.org/10.62647/Keywords:
Machine Learning (ML), Pycaret, Accuracy, Health Pattern Check, Extreme Gradient Boost (XGBoost)Abstract
Examining patient health through the lens of Machine Learning (ML) was the primary objective of this research. We accomplished this by using the auto-ML-Pycaret and Extreme Gradient Boost (XGBoost) classifiers. This article details the three-step process we used to construct the XGBoost model: data analysis, feature engineering, and model development. Data science tools like Google Colab (GC) and the Jupyter notebook were used for these assignments. Our next topic is the auto-ML-Pycaret model, a powerful instrument for ML applications. At last, we compare the two models' performance according to their accuracy levels. We got 88% accuracy with the auto ML Pycaret model, whereas the initial ML model was 87% accurate. When comparing the XGBoost and auto-ML Pycaret models, we found that the latter had superior performance in terms of accuracy percentages and time factor.
Downloads
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.