Diabetes is the body’s inability to efficiently break down sugar or secrete enough insulin required to process glucose, which supports normal bodily functions. Diabetes, as a prevalent chronic disorder, has contributed to numerous underlying health challenges among its carriers and is classified by the WHO as the world’s deadliest disease and silent killer. Its non-communicable nature makes early diagnosis difficult, allowing progression through various stages: type I, type II, pre-diabetes, and gestational. This challenge is further compounded by the imbalanced nature of diabetes datasets, which leads to high misclassification, poor generalization, and reduced accuracy. This study predicts diabetes using a bi-directional long short-term memory (BiLSTM) model applied to two datasets: (a) PIMA Indian Diabetes and (b) Iraqi Society Dataset, to evaluate the impact of six known balancing techniques and assess their effectiveness. Results show that for PID, the SMOTE-Tomek fused BiLSTM outperforms other balancing schemes with F1, Accuracy, Precision, Recall, and Specificity scores of 0.9182, 0.9198, 0.9128, 0.9248, and 0.9208, respectively. For ISD, it also achieves the best performance with values of 0.9367, 0.9369, 0.9386, 0.9388, and 0.9313, respectively. Other balancing approaches yielded F1 scores ranging from [0.6751 to 0.9347], accuracy [0.684 to 0.9358], Precision [0.6851 to 0.9296], Recall [0.6639 to 0.9356], and specificity [0.6658 to 0.9298]. These results imply that BiLSTM is resilient to the vanishing gradient problem and can effectively classify diabetes cases with enhanced performance.

High blood pressure (or hypertension) is a causative disorder to a plethora of other ailments – as it succinctly masks other ailments, making them difficult to diagnose and manage with a targeted treatment plan effectively. While some patients living with elevated high blood pressure can effectively manage their condition via adjusted lifestyle and monitoring with follow-up treatments, Others in self-denial leads to unreported instances, mishandled cases, and in now rampant cases – result in death. Even with the usage of machine learning schemes in medicine, two (2) significant issues abound, namely: (a) utilization of dataset in the construction of the model, which often yields non-perfect scores, and (b) the exploration of complex deep learning models have yielded improved accuracy, which often requires large dataset. To curb these issues, our study explores the tree-based stacking ensemble with Decision tree, Adaptive Boosting, and Random Forest (base learners) while we explore the XGBoost as a meta-learner. With the Kaggle dataset as retrieved, our stacking ensemble yields a prediction accuracy of 1.00 and an F1-score of 1.00 that effectively correctly classified all instances of the test dataset.

The digital revolution frontiers have rippled across society today – with various web content shared online for users as they seek to promote monetization and asset exchange, with clients constantly seeking improved alternatives at lowered costs to meet their value demands. From item upgrades to their replacement, businesses are poised with retention strategies to help curb the challenge of customer attrition. The birth of smartphones has proliferated feats such as mobility, ease of accessibility, and portability – which, in turn, have continued to ease their rise in adoption, exposing user device vulnerability as they are quite susceptible to phishing. With users classified as more susceptible than others due to online presence and personality traits, studies have sought to reveal lures/cues as exploited by adversaries to enhance phishing success and classify web content as genuine and malicious. Our study explores the tree-based Random Forest to effectively identify phishing cues via sentiment analysis on phishing website datasets as scrapped from user accounts on social network sites. The dataset is scrapped via Python Google Scrapper and divided into train/test subsets to effectively classify contents as genuine or malicious with data balancing and feature selection techniques. With Random Forest as the machine learning of choice, the result shows the ensemble yields a prediction accuracy of 97 percent with an F1-score of 98.19% that effectively correctly classified 2089 instances with 85 incorrectly classified instances for the test-dataset.

Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

Blockchain platforms propagate into every facet, including managing medical services with professional and patient-centered applications. With its sensitive nature, record privacy has become imminent with medical services for patient diagnosis and treatments. The nature of medical records has continued to necessitate their availability, reachability, accessibility, security, mobility, and confidentiality. Challenges to these include authorized transfer of patient records on referral, security across platforms, content diversity, platform interoperability, etc. These, are today – demystified with blockchain-based apps, which proffers platform/application services to achieve data features associated with the nature of the records. We use a permissioned-blockchain for healthcare record management. Our choice of permission mode with a hyper-fabric ledger that uses a world-state on a peer-to-peer chain – is that its smart contracts do not require a complex algorithm to yield controlled transparency for users. Its actors include patients, practitioners, and health-related officers as users to create, retrieve, and store patient medical records and aid interoperability. With a population of 500, the system yields a transaction (query and https) response time of 0.56 seconds and 0.42 seconds, respectively. To cater to platform scalability and accessibility, the system yielded 0.78 seconds and 063 seconds, respectively, for 2500 users.