Until recently, traditional machine learning techniques (TMLTs) such as multilayer perceptrons (MLPs) and support vector machines (SVMs) have been used successfully for churn prediction, but with significant efforts expended on the configuration of the training parameters. The selection of the right training parameters for supervised learning is almost always experimentally determined in an ad hoc manner. Deep neural networks (DNNs) have shown significant predictive strength over TMLTs when used for churn predictions. However, the more complex architecture of DNNs and their capacity to process huge amounts of non-linear input data demand more time and effort to configure the training hyperparameters for DNNs during churn modeling. This makes the process more challenging for inexperienced machine learning practitioners and researchers. So far, limited research has been done to establish the effects of different hyperparameters on the performance of DNNs during churn prediction. There is a lack of empirically derived heuristic knowledge to guide the selection of hyperparameters when DNNs are used for churn modeling. This paper presents an experimental analysis of the effects of different hyperparameters when DNNs are used for churn prediction in the banking sector. The results from three experiments revealed that the deep neural network (DNN) model performed better than the MLP when a rectifier function was used for activation in the hidden layers and a sigmoid function was used in the output layer. The performance of the DNN was better when the batch size was smaller than the size of the test set data, while the RemsProp training algorithm had better accuracy when compared with the stochastic gradient descent (SGD), Adam, AdaGrad, Adadelta, and AdaMax algorithms. The study provides heuristic knowledge that could guide researchers and practitioners in machine learning-based churn prediction from the tabular data for customer relationship management in the banking sector when DNNs are used.

Depression is a serious worldwide public health problem, and its diagnosis still remains a challenge in the medical community. The difficulties of detecting depression are largely due to its high comorbid factor. Given the reciprocal relationship between depression and physical illness, mental health professionals have called for a diagnostic approach that identifies and evaluates each disorder, concurrently. This paper reports the findings of a study based on data collected in Nigeria to investigate the simultaneous identification of depression and co-occuring physical illness using a multi-dimensional Bayesian network classification approach. The predictive model would be useful to clinicians of all categories in Nigeria in overcoming the challenges of depression diagnosis caused by its frequent co-occurence with physical illnesses. The benefits of this approach are demonstrated with anonymised multi-dimensional depression dataset comprising 1090 instances, 22 symptoms, and two class attributes. The results, are also described

With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of comprehending the input data information, the model, and model’s decisions quite challenging. This lack of transparency constitutes both a practical and an ethical issue. For the present study, it is a major drawback to the deployment of deep learning methods mandated with detecting patterns and prognosticating Alzheimer’s disease. Many approaches presented in the AI and medical literature for overcoming this critical weakness are sometimes at the cost of sacrificing accuracy for interpretability. This study is an attempt at addressing this challenge and fostering transparency and reliability in AI-driven healthcare solutions. The study explores a few commonly used perturbation-based interpretability (LIME) and gradient-based interpretability (Saliency and Grad-CAM) approaches for visualizing and explaining the dataset, models, and decisions of MRI image-based Alzheimer’s disease identification using the diagnostic and predictive strengths of an ensemble framework comprising Convolutional Neural Networks (CNNs) architectures (Custom multi-classifier CNN, VGG-19, ResNet, MobileNet, EfficientNet, DenseNet), and a Vision Transformer (ViT). The experimental results show the stacking ensemble achieving a remarkable accuracy of 98.0% while the hard voting ensemble reached 97.0%. The findings present a valuable contribution to the growing field of explainable artificial intelligence (XAI) in medical imaging, helping end users and researchers to gain deep understanding of the backstory behind medical image dataset and deep learning model’s decisions.

Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which MRI-based AI systems are increasingly used for diagnosis and prognosis. However, many published approaches remain misaligned with the requirements of trustworthy clinical use. Predicted risks are often poorly calibrated, explanations are frequently limited or non-actionable, guideline-aligned reporting is uncommon, and longitudinal prediction is inconsistently evaluated. In this paper, we conduct a PRISMA-guided structured review with scoping-style breadth of MRI-centric AI methods for AD diagnosis. This design supports a theme-based synthesis across heterogeneous study designs and is intended to summarize the current evidence base and derive practical design requirements for next-generation, clinically oriented pipelines that integrate calibrated staging, explainable outputs, and longitudinal risk modeling. Methods: Searches were conducted across Scopus, PubMed/PMC, and arXiv/bioRxiv (2014–2026; English; human AD/MCI imaging) and were supplemented by backward and forward snowballing. These searches yielded 2460 records. After deduplication, screening, and full-text eligibility assessment, 90 papers were included in the final synthesis. The included literature was organized into thematic streams spanning counterfactual reasoning and explainable AI (XAI), vision–language approaches for report and caption generation, longitudinal and survival-style modeling, and multimodal fusion and transformer-based methods combining MRI with clinical variables and other biomarkers. Vision–language methods were considered together with retrieval-augmented paradigms. Results: Key findings are that the field has shifted toward transformer architectures and multimodal fusion and shows increased interest in richer explanation mechanisms. Nevertheless, calibration metrics and robustness checks are inconsistently reported, external site-held-out validation and subgroup analyses remain relatively uncommon, and guideline-aligned structured reporting with explicit numeric provenance is rare. Vision–language and retrieval-augmented reporting methods are far more mature in general radiology than in AD MRI, highlighting a translational opportunity. Conclusions: Based on these findings, we recommend standardized reporting of classification calibration and longitudinal risk calibration, stronger site-held-out validation with subgroup robustness evaluation, clinically meaningful counterfactuals, and guideline-aligned reporting with reproducible numeric provenance embedded within reproducible pipelines.