Optimizing Diabetes Prediction Models for Enhanced Health Data Processing

Title

Optimizing Diabetes Prediction Models for Enhanced Health Data Processing

Creator

Chatterjee, Soham; Gupta, Ritwika Das; Ramadani, Lauresha

Description

Diabetes prediction is crucial for early intervention and personalized treatment. This study uses a multimodal strategy, including prediction algorithms, downsampling, feature engineering, exploratory data analysis (EDA), cross-validation, and classification techniques. EDA is used to understand diabetes-specific features, while downsampling ensures fair representation of instances with and without diabetes. Classification algorithms categorize people into appropriate diabetes risk groups using machine learning. Cross-validation evaluates predictive models in various data scenarios. The study emphasizes the value of specialized methods and domain-specific expertise in diabetes prediction, emphasizing the need for accurate risk assessment in healthcare decision-making and the potential for proactive interventions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Electrical Engineering;Volume;1269;pp.129-140

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Classification techniques; Cross-validation; Diabetes prediction; Downsampling; EDA; Feature engineering; Prediction

Coverage

Chatterjee S., Department of Statistics and Data Science, CHRIST Deemed to be University, Bangalore, India; Gupta R.D., Department of Statistics and Data Science, CHRIST Deemed to be University, Bangalore, India; Ramadani L., Department of Computer Science, AAB College, Pristina, Kosovo

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 18761100; ISBN: 978-981979514-7;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-9515-4_10

https://www.scopus.com/pages/publications/105003894981?origin=resultslist