TECHNICAL PAPER A novel method for diabetes classification and prediction with Pycaret Pawan Whig 1 • Ketan Gupta 2 • Nasmin Jiwani 2 • Hruthika Jupalle 3 • Shama Kouser 4 • Naved Alam 5 Received: 21 August 2022 / Accepted: 15 May 2023 Ó The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023 Abstract The incredible advances in biotechnology and public healthcare infrastructures have resulted in a massive output of vital and sensitive healthcare data. Many fascinating trends are discovered using intelligent data analysis approaches for the early identification and prevention of numerous severe illnesses. Diabetes mellitus is a highly hazardous condition since it leads to other deadly diseases such as heart, kidney, and nerve damage. In this research study, a low code Pycaret machine learning technique is used for diabetes categorization, detection, and prediction. On applying Pycaret various classifiers having different accuracies are produced and shown in the result section. After hyper tuning of various classifiers, it is found that the gradient boosting classifier is best further tuned and an accuracy of about 90% is achieved which is the highest among all existing ML classifiers. 1 Introduction With the world’s population rising, it’s more important than ever to build mechanisms to improve health and alleviate mounting worries (World Health Organization 2019). The construction of such a system is getting more efficient as scientific research advances (Williams et al. 2020). Healthcare systems are meant to meet people’s needs for good health and to identify and diagnose diseases and disorders more accurately and efficiently than tradi- tional approaches suggest (American Diabetes Association 2014). Patients are frequently worried about the quality of the healthcare system and treatment services that are offered (Acciaroli et al. 2018). People with chronic con- ditions benefit more directly from changes in healthcare systems, and this group makes up the bulk of those affected by diseases like diabetes and blood sugar problems (Tun et al. 2017; Davies et al. 2018). Public health is a crucial aspect of ensuring the well- being of communities and preventing the spread of diseases (Bruen et al. 2017). Governments allocate a significant portion of their national budgets towards public health and measures such as vaccination have contributed to an increase in life expectancy (Wadhwa and Babber 2020; Tedeschi and Sciancalepore 2019). However, the rise of chronic and hereditary conditions in recent years has become a major public health issue. One such example is diabetes mellitus, which can cause severe damage to vital organs such as the heart, kidneys, and nerves (Schaar et al. 2021; Arnold 2021; Kim and Huh 2021; Ali et al. 2021; Saji et al. 2021). As per the American Diabetes Latest Statistics 2021, diabetes affects one out of every ten persons in the United States, then novel belongings of diabetes 1 and 2 consume grown dramatically among young people. Because health care is a crucial backbone of strong humanity, the issue is vital to use the functionality of analytical modeling and AI to create novel techniques for use in health systems to & Pawan Whig pawanwhig@gmail.com Ketan Gupta ketan1722@gmail.com Nasmin Jiwani nasminjiwani@gmail.com Hruthika Jupalle hruthikajupalle@gmail.com Shama Kouser kouser609@gmail.com Naved Alam navedfzd@gmail.com 1 Vivekananda Institute of Professional Studies, New Delhi, India 2 University of The Cumberland, Williamsburg, USA 3 Sardar Vallabhbhai National Institute of Technology, Surat, India 4 Department of Computer Science Jazan University, Riyadh, Saudi Arabia 5 Jamia Hamdard University, New Delhi, India 123 Microsystem Technologies https://doi.org/10.1007/s00542-023-05473-2