# Prediction of Diabetes Complications Using Computational Intelligence Techniques
## 2 Literature Review
Essentially, if the strain imposed on the pavement structure is below the FEL, then no fatigue damage will accumulate and the pavement will have an infinite fatigue life [21]. The use of radiotracer injection is a common technique for measuring fluid flow rates and velocities. By injecting a small amount of a radioactive tracer into the fluid, the tracer's movement can be tracked and used to determine flow rates and velocities. The authors of the article mentioned utilised this technique. They combined it with an ANN model to accurately measure both the density and velocity of fluids in various pipe diameters. Artificial neural networks are a powerful tool for data analysis and prediction. Their use in this context allowed for precise measurements of the fluid parameters, without needing additional sensors or equipment. By training the ANN model using simulated data, the authors could accurately predict fluid density and velocity in different pipe diameters, providing a cost-effective and reliable method for fluid measurement in the oil and petroleum industries [22]. There has been significant progress in developing and testing nonstandard specimen geometries to measure fracture toughness data that are more applicable to defect assessments and fitness-for-service (FFS) analyses of structural components with crack-like flaws under low constraint conditions. These efforts were motivated by a better understanding of the potential, strong dependency of fracture toughness on crack geometry, loading type, and material strain hardening behaviour [23]. Tracking the growth trends of different firm sizes in the DB sector requires a more comprehensive analysis of data, beyond just revenue data. For example, the number and size of contracts awarded to DB firms of different sizes could provide insights into growth trends. Additionally, a more detailed analysis of industry trends, such as the demand for DB services in different sectors, could shed light on the growth prospects for firms of different sizes [24]. Using machine learning algorithms to build a model for early detection of diabetes is indeed a promising approach, given the increasing availability of healthcare data and the potential benefits of early diagnosis and intervention. The choice of the logistic regression classification algorithm to predict the presence of type 2 diabetes is appropriate, as this algorithm is a commonly used and well-understood approach in medical applications. Additionally, the use of the PIMA Indian Diabetic Dataset is suitable, as this dataset is widely used in the research community and provides a good benchmark for evaluating the performance of the developed model. The use of machine learning algorithms and healthcare data to develop models for early detection of diabetes has the potential to improve healthcare outcomes and reduce the burden of diabetes-related complications. However, it is important to ensure the accuracy and reliability of these models, through proper validation and verification in clinical settings, and to address potential issues related to data privacy and security [25]. The proposed study addresses an important issue related to the long-term prediction of type 2 diabetes, which is a crucial step towards prevention and early intervention. By identifying the risk-factors responsible for the future development of diabetes, the study can help individuals take preventive measures and make lifestyle changes to reduce the risk of developing diabetes. The use of two novel feature extraction approaches is also noteworthy, as this can help identify the most relevant and discriminative risk-factors for predicting diabetes. Applying a machine learning pipeline for the long-term prediction of diabetes is also appropriate, as this can potentially improve the accuracy and reliability of the developed model. The proposed study can contribute to the development of effective screening and prediction tools for type 2 diabetes, which can potentially reduce the burden of this costly and burdensome metabolic disorder.