A Comprehensive Analysis of Diabetes Risk Prediction Using Logistic Regression
  IJCTT-book-cover
 		          
 
© 2024 by IJCTT Journal
Volume-72 Issue-11
Year of Publication : 2024
Authors : Asish Pradhan
DOI :  10.14445/22312803/IJCTT-V72I11P122

How to Cite?

Asish Pradhan, "A Comprehensive Analysis of Diabetes Risk Prediction Using Logistic Regression," International Journal of Computer Trends and Technology, vol. 72, no. 11, pp. 192-219, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I11P122

Abstract
This study aimed to develop a predictive model for diabetes risk using a combination of demographic, examination, diet, and laboratory data. The dataset was processed through ETL (Extract, Transform, Load) and EDA (Exploratory Data Analysis) to identify potential correlations. A logistic regression model was built and evaluated using various metrics, achieving an accuracy of approximately 93%. The results indicate that the model can accurately predict diabetes risk, making it a valuable tool for healthcare professionals. The study demonstrates a comprehensive approach to building a predictive model for diabetes risk using a multidimensional dataset with potential applications in healthcare.

Keywords
Diabetes prediction, Exploratory data analysis, Healthcare data analysis, Logistic regression, Machine learning, Monte Carlo simulation.

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