# Prediction of Diabetes Complications Using Computational Intelligence Techniques
###### Abstract
**Classification and regression trees (CART):** CART is a decision tree algorithm that can be used for both classification and regression tasks. It works by recursively splitting the data into smaller subsets, based on the values of different features, until a stopping criterion is met. **Random forest (RF):** RF is an ensemble method that combines multiple decision trees to improve the performance and reduce the overfitting of individual trees. It works by creating multiple random samples of the training data and training a decision tree on each sample, then aggregating the results to make predictions. **Support vector machines (SVM):** SVM is a robust classification algorithm that finds the hyperplane that best separates the classes in the data. Using different kernel functions, it can handle linear and non-linearly separable data. **XGBoost:** XGBoost is an optimised gradient-boosting algorithm for classification and regression tasks. It works by iteratively adding weak learners to the model and adjusting the weights of misclassified samples to improve overall performance. **LightGBM:** LightGBM is another optimised gradient boosting algorithm designed to be fast and efficient. It uses a histogram-based algorithm to split the data and reduce memory usage during training. The choice of computational intelligence technique for predicting diabetes complications depends on the specific requirements of the application, available data, and computational resources. While some methods are more interpretable and easier to implement,others can handle more complex relationships in the data and require more computational resources. It is essential to evaluate the performance of different methods on independent datasets and choose the one that best meets the specific application's needs. Diabetes is a common chronic disease that affects millions of people worldwide and is a leading cause of morbidity and mortality. It is also a complex disease with various complications that can lead to significant health and economic burdens. The prediction of diabetes complications is a challenging task, and it requires the analysis of various factors, including clinical, genetic, and environmental factors. The use of computational intelligence techniques for predicting diabetes complications has shown promising results, and it has the potential to improve patient outcomes and reduce the burden of the disease. Moreover, there is a significant amount of data available on diabetes and its complications, which can be used to train and test predictive models. This data includes clinical data from electronic health records, genomic data, and lifestyle data, such as diet and exercise habits. Therefore, diabetes provides a rich and diverse dataset that can be used to develop and evaluate computational intelligence techniques. The choice to focus on diabetes to predict complications, using computational intelligence techniques, is motivated by its prevalence, complexity, and the availability of rich data sources. However, the same techniques can be applied to other chronic diseases with complex pathologies and available datasets to improve patient outcomes and reduce the burden of the disease. The selection of AI and ML models for predicting diabetes complications depends on the specific goals of the application, the type and size of the available data, and the desired level of interpretability of the model. The goal of this research should be considered when selecting an AI or ML model. A binary classification model, such as logistic regression, decision tree, or SVM, may be appropriate to predict the probability of a patient developing a particular complication. If the goal is to predict the severity of a complication, a regression model, such as linear regression or neural network, may be more suitable. The type and size of data available for training and testing the model should also be considered.