Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2024 09 08 918 918 Vandana Bavkar Bhivarabai Sawant College of Engineering & Research, Narhe, Pune, India Arundhati A. Shinde Bharati Vidyapeeth (Deemed to be University), 2024 01 05 2024 07 26 Purpose: Diabetes, resulting from insufficient insulin production or utilization, causes extensive harm to the body. The conventional diagnostic methods are often invasive. The classification of diabetes is essential for effective management. The progression in research and technology has led to additional classification approaches. Machine Learning (ML) algorithms have been deployed for analyzing the huge dataset and classifying diabetes. Materials and Methods: The classification and the regression of diabetic and non-diabetic are performed using the XGBoost mechanism. On the other hand, the proposed class-centric Focal XG-Boost is applied to elevate the model performance by measuring the similarity among the features. The prediction of the model is based on the classification and regression rates of diabetic and non-diabetic individuals, which are anticipated using applicable and effectual metrics to estimate their working performance. The dataset used in the Class-Centric Focal XG Boost model is attained using the Arduino Uno Kit. The data collection is done under a sampling rate of 100 Hz. The data are gathered from Bharati Hospital Pathology Laboratories, located in Pune. Results: The inclusive outcomes of the proposed model with their appropriate Exploratory Data Analysis (EDA) among classification and regression, with the suitable dataset used in the study are exemplified. Conclusion: The proposed Class-Centric Focal XG Boost model has numerous advantages and is less delicate to the hyperparameters than the conventional XGBoost algorithm. As a part of the real-time application of the Class-Centric Focal XG Boost model, the model can be utilized in other communicable and communicable disease classification and detection. https://fbt.tums.ac.ir/index.php/fbt/article/view/918 https://fbt.tums.ac.ir/index.php/fbt/article/download/918/440