Year: 2024
Type: article
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.
Author: Asish Pradhan
Institution: DELL (United States)
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4406960373