Transparent Peer Review By Scholar9
Patient Health Analysis Using Machine Learning
Abstract
Aim of this study was to analyze patient health using Machine Learning (ML). To do this, we used the Extreme Gradient Boost (XGBoost) classifier and auto-ML-Pycaret techniques. The sequential procedure we followed for the XGBoost model is data analysis, feature engineering, and model building, which are discussed in this paper. For these tasks, we used data :is an excellent tool for ML tasks. Finally, a performance comparison is performed between the two models based on their accuracy levels. The accuracy rate for the first ML model was 87%, and for the auto ML Pycaret model, we achieved 88% accuracy. Based on the accuracy percentages and time factor, we observed that the auto-ML Pycaret model performed better than the XGBoost model.
Shreyas Mahimkar Reviewer
17 Sep 2024 03:57 PM
Approved
Relevance and Originality
The study is highly relevant, focusing on analyzing patient health using advanced Machine Learning techniques. It explores the use of Extreme Gradient Boost (XGBoost) and auto-ML-Pycaret to enhance predictive accuracy in healthcare. The originality of the Research Article lies in its comparative analysis of these two methods, providing valuable insights into their effectiveness for health data analysis.
Methodology
The methodology involves data analysis, feature engineering, and model building using XGBoost and auto-ML-Pycaret. While the approach is solid, the description of data preprocessing and feature selection could be more detailed. Expanding on how these methods were implemented and tuned would offer clearer insights into the experimental setup.
Validity & Reliability
The accuracy rates reported (87% for XGBoost and 88% for auto-ML-Pycaret) suggest robust performance. However, the study lacks a detailed examination of the validation process and potential biases. Including information on cross-validation techniques and testing across diverse datasets would strengthen the validity and reliability of the results.
Clarity and Structure
The Research Article is clear in presenting its aim and comparing model performance. Nonetheless, the description of the methodology could benefit from more precise language and structure. Providing a step-by-step breakdown of the processes used for each model would enhance readability and understanding.
Result Analysis
The paper reports a marginal improvement in accuracy with the auto-ML-Pycaret model. However, the analysis would benefit from more in-depth results, such as confusion matrices or ROC curves, to better illustrate model performance. Additionally, discussing the implications of the accuracy differences and potential factors influencing them would provide a more comprehensive result analysis.
IJ Publication Publisher
Done Sir
Shreyas Mahimkar Reviewer