# Prediction of Diabetes Complications Using Computational Intelligence Techniques
###### Abstract
Inductive learning involves taking instances and generalising them, while deductive learning involves deducing new knowledge from existing facts and understanding. It involves finding patterns and rules from large databases and using them to create computer programs. Machine learning is the study of how to improve the performance of computer systems by learning from experience. It often follows the same principles as human learning. There are various strategies in the literature for identifying and diagnosing diabetes using machine learning methods [5]. Diabetes mellitus is a disease that is one of the leading causes of death globally and can lead to severe complications, such as renal failure, blindness, and heart disease. This study suggests using data mining techniques to predict diabetes, which can help in the early identification and management of the disease, thus reducing its associated morbidity and mortality [6]. Data mining tools can assist medical professionals by helping them make accurate disease diagnoses and treatment decisions while reducing specialists' workloads. Diabetes is a chronic disease affecting millions of people worldwide and can lead to complications if left untreated or poorly managed. Early prediction of diabetes compli  
Figure 1: Diabetes types. cations is crucial to provide timely interventions and prevent or delay the onset of these complications. Computational intelligence techniques can be used to develop predictive models for diabetes complications, leveraging the vast amounts of data available from electronic health records, wearable devices, and other sources. One popular approach is to use machine learning algorithms to build predictive models that can identify patterns in the data and predict the likelihood of complications. For example, decision trees, logistic regression, support vector machines, and neural networks are popular machine learning algorithms used for this purpose. Deep learning is another computational intelligence technique that has shown promise in predicting diabetes complications. Convolutional neural networks and recurrent neural networks are popular deep learning architectures for medical image analysis and time-series data analysis. Deep learning models can automatically learn complex patterns in the data, and with enough data, they can often outperform traditional machine learning methods. In addition to machine learning and deep learning, other computational intelligence techniques, like fuzzy logic and genetic algorithms, have also been used for predicting diabetes complications. Fuzzy logic can be used to model uncertainty and imprecision in the data, while genetic algorithms can be used to optimise the parameters of the predictive models. Computational intelligence techniques for predicting diabetes complications hold great promise for improving the quality of care for people with diabetes. However, it is essential to ensure that the models are validated on independent datasets and that their performance is clinically relevant before they are used in practice. Additionally, it is critical to consider the ethical and social implications of using such models, such as potential biases and privacy concerns. Several different computational intelligence techniques can be used to predict diabetes complications. The feasibility of these methods can vary depending on the specific application, available data, and computational resources. Here are some of the most commonly used methods and their feasibility for this task:  
**Logistic regression (LR):** LR is a classification algorithm used to predict the probability of a binary outcome (e.g., yes or no). It works by fitting a regression model to the data and then applying a sigmoid function to the output to convert it to a probability. **K-nearest neighbors (KNN):** KNN is a simple classification algorithm that finds the k nearest neighbours of a new data point and assigns it to the most common class among its neighbours.