# Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence
## 1 Introduction
It is a field that is comprehensively and inalienably specified with flag handling most notably through data-driven learning systems [21; 22] and has rapidly obtained its application in several daily life applications, such as healthcare, business, and many more. ML algorithms are valuable for disease detection [23]. In this work, we present an intelligent system for diabetes prediction using the PIMA Indians Diabetes dataset, and ML algorithms, such as K-nearest neighbors, decision tree classifier, deep learning (multilayer perceptron (MLP)), support vector machine (SVM), random forest classifier, an AdaBoost classifier, and logistic regression. The motivation for automatic prediction of diabetes in healthcare systems is to improve early detection and intervention of the disease. By utilizing machine learning and other predictive methods, healthcare systems can identify individuals at risk for diabetes before symptoms appear, allowing for early intervention and prevention of serious complications. Additionally, automatic prediction can help improve the efficiency and cost-effectiveness of diabetes management by reducing the need for manual screening and allowing healthcare providers to focus on those individuals most at risk. Furthermore, this approach can also help to reduce health disparities by identifying and targeting high-risk populations that may not have access to traditional screening methods. Overall, the goal is to improve the overall health outcomes for individuals with diabetes and reduce the burden of the disease on the healthcare system. Healthcare systems for diabetes play a crucial role as a starting point for other health monitoring. Diabetes is a chronic condition that often leads to other serious health complications such as heart disease, kidney failure and blindness. By implementing effective and efficient systems for the management of diabetes, healthcare providers can not only improve the health outcomes for individuals with diabetes, but also identify and intervene in the early stages of other chronic conditions. Additionally, by using diabetes as a starting point, healthcare systems can learn from the methods and strategies that have been successful in managing diabetes and apply them to other chronic conditions. This can help improve the overall health of the population and reduce the burden of chronic disease. Figure 2: The main causes of diabetes. Healthcare systems for diabetes can be generalized to other types of health monitoring by utilizing similar frameworks and methods for early detection and intervention. For example, implementing regular check-ups and screenings for other chronic conditions, such as heart disease or hypertension, can help identify and manage these conditions in their early stages. Additionally, utilizing the same data-collection and analysis techniques, such as machine learning and predictive modeling, can aid in identifying individuals at risk for these conditions. Furthermore, providing education and resources for individuals to manage their overall health can also help in preventing and managing other chronic conditions. By adopting a holistic approach to healthcare that focuses on early detection and intervention, healthcare systems can effectively manage and prevent a variety of chronic conditions. This paper aims to contribute to the knowledge according to the following points:  
* Automatic prediction of diabetes in healthcare systems is to improve early detection and intervention of the disease. Utilizing machine learning and other predictive methods, healthcare systems can identify individuals at risk for diabetes before symptoms appear. * Help to improve the efficiency and cost-effectiveness of diabetes management, allowing healthcare providers to focus on individuals most at risk, and help to reduce health disparities by identifying and targeting high-risk patients. * Implement an effective and efficient system for the management of diabetes to improve the overall health of the population and reduce the burden of chronic disease.