Patients’ Health Analysis using Machine Learning

Authors

  • Mr.Abdul Rais Author
  • Abdul Rizwan Naveed Author
  • Beeram Manichandu Author
  • Shaik Abdullah Abdul Jabbar Author

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.

 

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Published

28-04-2025

How to Cite

Patients’ Health Analysis using Machine Learning. (2025). International Journal of Information Technology and Computer Engineering, 13(2), 946-954. https://doi.org/10.62647/