## 6 Conclusions and Future Work  
In real life, a model for medical diagnosis should care about accuracy and how certain the prediction is. Diabetes is considered a major chronic disease that requires stability of the model and considers uncertain areas. In turn, if the uncertainty is too high, a doctor would consider this in his decision process, which is why we consider uncertainty classification rather than classification only. This study contains valuable information for classifying women into two different groups, diabetics and non-diabetics, using different supervised classification models. TFKNN and fuzzy KNN produced the best classification performances in terms of evaluation metrics such as accuracy, precision, recall, F1 score, AUC, and average weighted ROC. In this study, the PIDD is well analyzed and preprocessed, and the TFKNN classification model is built and boosted by hyperparameter optimization to predict diabetes in PIDD versus other classification models. Although the KNN approach achieves a higher micro- and macro-average ROC curves, the TFKNN model is the superior classifier for diabetes prediction in the PIDD based on accuracy, precision, sensitivity, and average AUC. The results indicated that the precision, recall, and F1 score of the TFKNN was 93.18%, while the obtained accuracy was 90.63. This is as compared with the fuzzy KNN, which achieved precision, recall, F1 score, and accuracy values of 91.30%, 95.45%, 93.32%, and 90.55, respectively. On the other hand, in terms of specificity, the proposed TFKNN achieved 85.00% compared with the fuzzy KNN, which achieved 79.49%. At the same time, the average area under the curve indicated that the proposed TFKNN achieved 94.13% compared with the Fuzzy KNN, which achieved 90.55%. These numerical results all refer to the superiority of the proposed TFKNN compared with recent state-of-art methodologies. However, the TFKNN classifier helps in handling uncertainty classification instead of crisp using a membership function that measures the estimation of loyalty for an instance to a certain class. Although the fuzzy KNN has the worst value(s) at the complexity levels, it is the best in practice in terms of evaluation metrics. Some limitations must be addressed, however, including the threshold election and hyperparameter optimization. Additionally, the uncertainty classification considers instances individually without awareness of global features and instances that are considered a challenge. In the future, we intend to support the classification model in an uncertainty mode with the explainability of machine learning and engage a global context during the learning process for medical diagnostic models of diabetes. Additionally, there is an intention to design a deep learning architecture for handling preprocessing, uncertainty classification, and hold-out validation and overcome the fuzzy KNN challenge(s) based on a scheduled work to re-arrange the membership function of fuzzy KNN to be referenced and pre-computed once as a lookup map. Conceptualization, H.S., M.Y.S., O.M.E. and M.A.E. ; methodology, H.S., M.Y.S., O.M.E., M.A.E., J.F.A.-A. and S.E. ; software, H.S., M.Y.S., O.M.E., J.F.A.-A. and S.E. ; validation, H.S., M.Y.S., O.M.E., M.A.E., J.F.A.-A. and S.E. ; formal analysis, H.S., M.Y.S., O.M.E., M.A.E., J.F.A.-A. and S.E. ; investigation, H.S., M.Y.S., O.M.E., M.A.E., J.F.A.-A. and S.E. ; resources, H.S., M.Y.S., O.M.E., J.F.A.-A. and S.E. ; data curation, H.S., M.Y.S., O.M.E., M.A.E., J.F.A.-A. and S.E. ; writing--original draft preparation H.S., M.Y.S., O.M.E., M.A.E., J.F.A.-A. and S.E. ; writing--review and editing. H.S., M.Y.S., O.M.E., M.A.E., J.F.A.- and S.E. ; visualization, H.S., M.Y.S., O.M.E., J.F.A.-A. and S.E. ; supervision, H.S., M.Y.S., O.M.E., M.A.E., J.F.A.-A. and S.E. ; project administration, H.S., M.Y.S., O.M.E., J.F.A.-A. and S.E. ; funding acquisition, H.S., M.Y.S., O.M.E., M.A.E., J.F.A.- and S.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding.