As a result, pregnant women could be affected by gestational diabetes, but some are more vulnerable than others. Women over the age of 25, with a family or personal history of diabetes, and who are overweight are all risk factors for diabetes [13; 14]. Based on the most recent epidemiological results [15], forecasts the global and regional effects of diabetes for 2017 and 2045. Notably, the hit rate will increase to 693 million by 2045. The high prevalence of diabetes has significant social, environmental, and growth consequences, especially in low- and middle-income countries. The WHO's objective is to encourage and support successful diabetes monitoring, prevention, and control efforts, particularly in poor and middle-income countries [16]. Fuzzy logic is close to human reasoning, as it can accommodate the inconsistencies present in medical diagnosis results. Fuzzy logic is a Boolean logic extension dependent on degrees of truth between 0 and 1, or true/false. The theme of degree, inaccuracy, linguistics, and observation underpins fuzzy logic [8]. Many studies have been introduced to predict diabetes [17]. The best rules were optimized using the cost estimation function after fuzzy classification rules were created [18]. In [8], fuzzy expert system (FES)-based data mining was used to analyze information from the available evidence that could be ambiguous and to propose linguistic concepts with large approximations as their key to medical texts. Recently, the speed of diabetes diagnosis has increased based on the integration of recent technologies such as deep learning and a novel optimized architecture of four Hadoop clusters for data classification using the multi-level MapReduce fuzzy classifier (MMR-FC) and MapReduce-modified density-based spatial clustering of applications with noise (MR-MDBSCAN) [18]. Figure 2: Major dimensions affecting diabetes. In this study, the PIDD is well analyzed and preprocessed. A fuzzy KNN classification model boosted by hyperparameter optimization is proposed to predict diabetes in the PIDD versus other classification models. Although KNN achieves higher micro-average and macro-average ROC curves, fuzzy KNN is the superior classifier for diabetes prediction in the PIDD based on accuracy, precision, sensitivity, and average AUC. The PIDD is analyzed to detect the relationship between the diabetes features and class labels and to detect the key problems of the given dataset, including outliers, missing values, normalization, feature importance, and imbalanced categories. The major contributions of this paper can be summarized as follows:  
* A reliable preprocessing batch procedure (s) is proposed to resolve the interference of feature impacts and contradictions, determining the discriminator plans of the binary category for dealing with the PIDD;
* Initial crisp logic rules are extracted from the processed data using a decision tree, Naive Bayes, and a basic KNN classifier (s) for diabetes classification;
* The training dataset is used as a base for resolving the uncertainty membership of each instance for reducing the misclassification coefficient cost and categorizing instance (s) reliably;
* A modified Fuzzy KNN classifier (TFKNN) is proposed that uses an uncertainty membership function instead of regular fuzzy membership or a classical member function for the KNN classifier (s);
* It is assumed that some performances lack hyperparameter values for the classification model, and a grid search optimizer is used to determine the tuned value(s) of the hyperparameter (s) for the fuzzy KNN, DT, NB, and the proposed TFKNN classifiers;
* The performance of the classifier (s) with the best values for model parameters using the PIDD regarding the classification metrics is benchmarked and validated. * A hybrid cross-hold-fold validation framework is applied to validate the proposed methodology using different classifiers, including uncertainties. The rest of the paper is organized as follows: the related work and the current contributions to classify diabetes are given in Section 2.