ItemBook Chapter

Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction

Title
Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction
Creator
Padmalal, S.; Arumugam, P.; Baby Anusha, M.; Bamane, Kalyan Devappa; Yamsani, Nagendar; Muppavaram, Kireet
Description
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.
Source
Machine Learning in Healthcare: Data-Driven Decisions, Predictive Modelling, Personalized Medicine;pp.1771-192
Date
01-01-2026
Publisher
De Gruyter
Subject
Diabetic prediction; Logistic regression; Machine learning; Random forest; Stacked ensemble model; Support vector machine; XGBoost
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-311154123-5; 978-311154107-5;
Format
online
Language
English
Type
Book chapter

Citation

Padmalal, S.; Arumugam, P.; Baby Anusha, M.; Bamane, Kalyan Devappa; Yamsani, Nagendar; Muppavaram, Kireet, “Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24487.

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