Breast cancer is the primary cause of death for women around the world, necessitating the development of highly accurate, interpreted, and technologically advanced predictive approaches to support early diagnosis and treatment. In this research, we introduce a deep learning (DL) model for predicting breast cancer using both public and private datasets. The model uses the internet of things (IoT) to improve data collection and real‐time monitoring, and it also uses the SMOTE method to resolve issues of class imbalance. The proposed model combines an explainable AI approach with SHAP values to ensure model interpretability. To identify the best DL algorithm for this method, we assess and compare six different DL algorithms: temporal convolutional networks (TCNs), neural factorization machines (NFMs), long short–term memory (LSTM) networks, recurrent neural networks (RNNs), gated recurrent units (GRUs), and deep kernel learning (DKL). IoT devices allow for the continuous acquisition of patient data, which, when integrated with our predictive models, improve the capacity for early detection. Reliable cancer detection relies on our method’s enhanced predictive accuracy and sensitivity. Furthermore, we offer crucial transparency in clinical settings by using SHAP to give detailed explanations of model decisions. By employing thorough statistical analysis and cross‐validation, we guarantee that our model is resilient and can be applied to various patient populations. The results show that our proposed IoT integrated method has the potential to improve prediction performance and boost confidence in AI‐powered medical diagnostics by making them more accessible and easier to use. From a performance perspective, the proposed approach, which uses the TCN algorithm and SMOTE, achieved the best accuracy for BC prediction. With the public dataset, the experimental results were 99.44%, 100.0%, 99.01%, 98.75%, 99.37%, and 99.89% for accuracy, sensitivity, specificity, precision, F1‐score, and AUC, respectively. The experimental results for accuracy, sensitivity, specificity, precision, F1‐score, and AUC using the private dataset were 97.33%, 93.33%, 100%, 100%, 96.55%, and 99.48%, respectively. On the other hand, with the combined datasets, the TCN algorithm achieved 100% for all performance metrics.

With the increasing prevalence of diabetes in Saudi Arabia, there is a critical need for early detection and prediction of the disease to prevent long-term health complications. This study addresses this need by using machine learning (ML) techniques applied to the Pima Indians dataset and private diabetes datasets through the implementation of a computerized system for predicting diabetes. In contrast to prior research, this study employs a semisupervised model combined with strong gradient boosting, effectively predicting diabetes-related features of the dataset. Additionally, the researchers employ the SMOTE technique to deal with the problem of imbalanced classes. Ten ML classification techniques, including logistic regression, random forest, KNN, decision tree, bagging, AdaBoost, XGBoost, voting, SVM, and Naive Bayes, are evaluated to determine the algorithm that produces the most accurate diabetes prediction. The proposed approach has achieved impressive performance. For the private dataset, the XGBoost algorithm with SMOTE achieved an accuracy of 97.4%, an F1 coefficient of 0.95, and an AUC of 0.87. For the combined datasets, it achieved an accuracy of 83.1%, an F1 coefficient of 0.76, and an AUC of 0.85. To understand how the model predicts the final results, an explainable AI technique using SHAP methods is implemented. Furthermore, the study demonstrates the adaptability of the proposed system by applying a domain adaptation method. To further enhance accessibility, a mobile app has been developed for instant diabetes prediction based on user-entered features. This study contributes novel insights and techniques to the field of ML-based diabetic prediction, potentially aiding in the early detection and management of diabetes in Saudi Arabia.