A novel multistage ensemble approach for prediction and classification of diabetes

Sarita Simaiya; Rajwinder Kaur; Jasminder Kaur Sandhu; Majed Alsafyani; Roobaea Alroobaea; Deema Mohammed Alsekait; Martin Margala; Prasun Chakrabarti

doi:10.3389/fphys.2022.1085240

A novel multistage ensemble approach for prediction and classification of diabetes

Front Physiol. 2022 Dec 19:13:1085240. doi: 10.3389/fphys.2022.1085240. eCollection 2022.

Authors

Sarita Simaiya^{1

2}, Rajwinder Kaur³, Jasminder Kaur Sandhu¹, Majed Alsafyani⁴, Roobaea Alroobaea⁵, Deema Mohammed Alsekait⁶, Martin Margala², Prasun Chakrabarti⁷

Affiliations

¹ Department of Computer Science and Engineering, Institute of Engineering and Technology, Chandigarh University, Mohali, Punjab, India.
² School of Computing and Informatics, University of Louisiana, Lafayette, LA, United States.
³ Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
⁴ Department Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
⁵ Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
⁶ Department of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
⁷ ITM (SLS) Baroda University, Vadodara, Gujarat, India.

Abstract

Diabetes mellitus is a metabolic syndrome affecting millions of people worldwide. Every year, the rate of occurrence rises drastically. Diabetes-related problems across several vital organs of the body can be fatal if left untreated. Diabetes must be detected early to receive proper treatment, preventing the condition from escalating to severe problems. Tremendous health sciences and biotechnology advancements have resulted in massive data that generated massive Electronic Health Records and clinical information. The exponential increase of electronically gathered information has resulted in more complicated, accurate prediction models that can be updated continuously using machine learning techniques. This research mainly emphasizes discovering the best ensemble model for predicting diabetes. A new multistage ensemble model is proposed for diabetes prediction. In this model, accuracy is predicated on the Pima Indian Diabetes dataset. The accuracy of the proposed ensemble model is compared with the existing machine learning model, and the experimental results demonstrate the performance of the proposed model in terms of higher Precision, f-measure, Recall, and area under the curve.

Keywords: clinical data processing; diabetes mellitus; ensemble modeling; health care; linear discriminant analysis; machine learning; prediction.