Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhancing Medical Decision Support Systems withtheTwo-Parameter Logistic Regression Model Subject The topic of the resource Liu estimator; Mean squared error; Multicollinearity; Ridge estimator; Two-parameter logistic regression Description An account of the resource 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. Creator An entity primarily responsible for making the resource Joshi H.; Chakraborty A. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 195-202. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-0975-5_17" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-0975-5_17</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197244049&amp;doi=10.1007%2F978-981-97-0975-5_17&amp;partnerID=40&amp;md5=f6ddcb6e05742c0b3768b7b61e0fd06f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197244049&amp;doi=10.1007%2f978-981-97-0975-5_17&amp;partnerID=40&amp;md5=f6ddcb6e05742c0b3768b7b61e0fd06f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981970974-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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