## 1 Introduction  
Diabetes is one of the frequent diseases that targets the elderly population worldwide. According to the International Diabetes Federation, 451 million people across the world were diabetic in 2017. The expectations are that this number will increase to affect 693 million people in the coming 26 years [1]. Diabetes is considered as a chronic disease associated with an abnormal state of the human body where the level of blood glucose is inconsistent due to some pancreas dysfunction that leads to the production of little or no insulin at all, causing diabetes of type 1 or cells to become resistant to insulin, causing diabetes of type 2 [2, 3]. The main cause of diabetes remains unknown, yet scientists believe that both genetic factors and environmental lifestyle play a major role in diabetes. Even though it's incurable, it can be managed by treatment and medication. Individuals with diabetes face a risk of developing some secondary health issues such as heart diseases and nerve damage. Thus, early detection and treatment of diabetes can prevent complications and assist in reducing the risk of severe health problems. Many researchers in the bioinformatics field have attempted to address this disease and tried to create systems and tools that will help in diabetes prediction. They either built prediction models using different types of machine learning algorithms such as classification or association algorithms. Decision Trees, Support Vector Machine (SVM), and Linear Regression were the most common algorithms [4, 5, 6]. Artificial Neural Network (ANN) is another type of machine learning technique. It is well-known for its high performance and accuracy. Furthermore, due to the increasing size and complexity of the data, Deep Learning (DL) has been introduced as an improvement to ANN. Recent studies that have used DL produced remarkable results [7; 8]. The accuracy rate produced by these methods varied. This has encouraged researchers to attempt improving the accuracy by either building models with classifiers that haven't been used or combine different classifiers [9; 10; 11]. The majority of the studies in the field of the dDiabetes prediction used the public Pima Indian Dataset obtained from the UCI repository. Some surveys have been published, but they are different from this study. For example, [12] discussed the well-known ML and DL techniques used in predicting diabetes. The authors described only the studies related to Decision Tree, Support Vector Machine, Artificial Neural Network, and some DL techniques. Reference [13] also surveyed the main well-known ML classification techniques to predict diabetes. Reference [14] investigated the ML techniques used in predicting different diseases, including diabetes, which is discussed by only five related works. Reference [15] also discussed the ML techniques used in predicting heart, breast cancer and diabetes diseases, where the authors discussed only two studies about diabetes prediction. In fact, all these surveys investigated the ML techniques used in predicting diabetes disease. However, only one survey [12] addressed the DL state-of-the art techniques that predicted diabetes disease. In addition, two reviews [12; 13] focused only on the diabetes disease, unlike [14; 15] which discussed several diseases including diabetes introduced by five studies in [14] and only two studies in [15]. This research paper discusses Machine and Deep Learning techniques in addition to combined models for the prediction of diabetes [16] published since 2013. Combined models are defined as a combination of two or more classifiers. For example, they can be a combination of two or more ML techniques, or ML with AI techniques. This paper provides a systematic review of the performances of different Machine and Deep Learning classifiers obtained from different papers collected in the last six years.