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 Faculty Publications Book Chapter Faculty Publications- Book Chapter 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/. Creator An entity primarily responsible for making the resource Padmalal, S.; Arumugam, P.; Baby Anusha, M.; Bamane, Kalyan Devappa; Yamsani, Nagendar; Muppavaram, Kireet Title A name given to the resource Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Machine Learning in Healthcare: Data-Driven Decisions, Predictive Modelling, Personalized Medicine;pp.1771-192 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1515/9783111541235-009" target="_blank" rel="noreferrer noopener">https://doi.org/10.1515/9783111541235-009</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028340536?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028340536?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Padmalal S., Department of Computer Science and Engineering, Mangalam College of Engineering, Ettumanoor, India; Arumugam P., Department of statistics, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, Abishekapatti, 627012, India; Baby Anusha M., Department Of CSE, Lakireddy Bali Reddy College of Engineering, Krishna, Andhra Pradesh, Mylavaram, 521230, India; Bamane K.D., Department of Computer Engineering, D.Y. Patil College of Engineering, Pune, Akurdi, India; Yamsani N., School of Computer Science and Artificial Intelligence, SR University, Telangana, Warangal, 506371, India; Muppavaram K., Deemed to be University, Hyderabad, India Description An account of the resource Early detection of chronic diseases like diabetes is very important for early treatment and effective management. This chapter describes a machine learning (ML) solution for predicting diabetes risk from clinical structured data and a case study is constructed on the PIMA Indian Diabetes dataset. The solution caters to the entire ML pipeline: problem formulation, preprocessing of data, feature selection (FS), model training, validation, and deployment issues. Different preprocessing techniques including missing value imputation, detection of outliers, and feature normalization were used for improving data quality. FS techniques like correlation analysis, recursive feature elimination, and selection based on domain knowledge were utilized to decrease the dimensionality of the data as well as model interpretability. Extensive comparison was conducted among widely used classification models like logistic regression (LR), random forest, support vector machine, and XGBoost. It was suggested to adopt a stacked ensemble model of LR, RF, SVM, and XGBoost that achieved better performance in terms of accuracy, precision, recall, and F1-score. The findings confirm the tremendous potential of ML to enable early diabetes diagnosis as an unobtrusive, data-driven, and scalable decision-making supporting system for physicians. This is the groundwork for the further development of clinically applicable artificial intelligence-based prediction models within real-world healthcare settings. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin. Subject The topic of the resource Diabetic prediction; Logistic regression; Machine learning; Random forest; Stacked ensemble model; Support vector machine; XGBoost Publisher An entity responsible for making the resource available De Gruyter Relation A related resource ISBN: 978-311154123-5; 978-311154107-5; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online