Enhancing Medical Decision Support Systems withtheTwo-Parameter Logistic Regression Model
- Title
- Enhancing Medical Decision Support Systems withtheTwo-Parameter Logistic Regression Model
- Creator
- Joshi H.; Chakraborty A.
- Description
- The logistic regression model is an invaluable tool for predicting binary response variables, yet it faces a significant challenge in scenarios where explanatory variables exhibit multicollinearity. Multicollinearity hinders the models ability to provide accurate and reliable predictions. To address this critical issue, this study introduces innovative combinations of Ridge and Liu estimators tailored for the two-parameter logistic regression model. To evaluate the effectiveness of the combination of ridge and Liu estimators under the two-parameter logistic regression, a real-world dataset from the medical domain is utilized, and Mean Squared Errors are employed as a performance metric. The findings of our investigation revealed that the ridge estimator, denoted as k4, outperforms other Liu estimators when multicollinearity is present in the data. The significance of this research lies in its potential to enhance the reliability of predictions for binary outcome variables in the medical domain. These novel estimators offer a promising solution to the multicollinearity challenge, contributing to more accurate and trustworthy results, ultimately benefiting medical practitioners and researchers alike. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
- Source
- Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 195-202.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Liu estimator; Mean squared error; Multicollinearity; Ridge estimator; Two-parameter logistic regression
- Coverage
- Joshi H., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, India; Chakraborty A., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981970974-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Joshi H.; Chakraborty A., “Enhancing Medical Decision Support Systems withtheTwo-Parameter Logistic Regression Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19420.