## 1 Introduction
### Early Diagnosis and Prediction of Diabetes  
Due to the extent of these complications, early assessment of the risk of developing them is of great importance. Traditionally, studies have been based on traditional statistical methods; however, with the development of machine learning (ML) technology, numerous novel models are now available for predicting the risk of developing diabetes and facilitating its diagnosis [17]. These emerging technologies are based on artificial intelligence (AI) and provide the opportunity to establish predictive models of personalised risk, which aim to optimise the potential of individualised or personalised (patient-centred) medicine, making it possible to go beyond traditional methodology. Their development and implementation are of great clinical importance, as it would allow the establishment of preventive (early detection) and predictive (estimation of the risk of development) measures to minimise the onset of the disease and future complications [17; 18]. Among the developed predictive techniques associated with ML are the following:  
* Neural networks: use a network of artificial neurons or nodes to solve learning problems [19]. * Algorithms, such as the following: \(\odot\)\(K\)-nearest neighbours: a simple, supervised classification method, highly useful for pattern recognition. It is based on the distance between two points, which represents the similarity between them. The algorithm identifies k points during model training and is then able to classify a new point according to its similarity to neighbouring points [20]. \(\odot\) Support vector machine (SVM): a classification and regression method, which defines the boundary between several data points by representing them graphically in a multidimensional space according to their characteristic values. It is intended to create a 'flat boundary', called a 'hyperplane', leading to highly homogeneous data partitions on both sides of the support vector [19]. \(\odot\) Decision tree: a predictive modelling technique used in statistics, data mining, and machine learning. It uses a simple stepwise representation of the data structure in the form of a sequence of decisions (tests) to predict an outcome or category. This hierarchical tree structure consists of a root node (which is the base of the tree and the most sensitive element when it is created and before it is exploited), branches (which feed internal nodes), internal nodes (where tests and evaluations are performed to form homogeneous subsets), and leaf nodes (or end nodes, which contain no branches and represent the possible outcomes of the dataset) [21]. \(\odot\) Random forests (RF): an algorithm used to improve prediction performance, combining versatility and power in a single machine learning approach. Datasets are formed, and decision trees are created for each set, yielding many trees in which their features predict a categorical outcome. These trees are then combined to produce a single result (Figure 1) [19; 20]. \(\odot\) Naive Bayes classifier (Naive Bayes): is a learning algorithm based on Bayes' theorem, used to solve classification problems. It is based on the idea that the occurrence of a particular feature is independent of the occurrence of other features [21]. \(\odot\) Others, such as logistic regression (LR), XGBoost or gradient boosting [22]. Figure 1: Example of a decision tree and random forest. More cutting-edge technologies, such as the Deep Learning Multilayer Perceptron (MLP), which is a special type of artificial neural network consisting of multiple layers of interconnected neurons. The first (input) layer has the same number of inputs as the total number of predictor variables, and the outputs of the neurons in this layer become inputs to the next, and so on, until the output layer is reached. The intermediate layers look for features associated with the data (Figure 2). All these methods must not only generate predictive models, but the prediction results must also be easily interpretable, accurate, and precise [19,21].