International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence

An Empirical Comparison of Machine Learning Algorithms for Breast Cancer Detection

Hamza Sabo Maccido * — 2025-07-13

Breast cancer continues to be a major global health challenge, necessitating the development of accurate and reliable diagnostic systems. This study presents a comparative evaluation of multiple machine learning classification models aimed at enhancing breast cancer detection. Three feature selection techniques which are Principal Component Analysis (PCA), Pearson Correlation Coefficient (PCC), and Backpropagation Neural Networks (BNN) were employed to reduce dimensionality and extract relevant features. The performance of six classifiers which are Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Naïve Bayes (NB), and Artificial Neural Network (ANN) was analyzed based on accuracy, precision, recall, specificity and f1-Score. Results show that among the evaluated classifiers, Random Forest and Support Vector Machine (SVM) consistently delivered the highest performance, with Random Forest achieving up to 98.8% accuracy and SVM up to 98.0%, particularly when trained on features selected through Backpropagation Neural Networks (BNN). K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) also demonstrated strong results, outperforming traditional models like Logistic Regression and Decision Tree in most scenarios. These outcomes underscore the superior classification capabilities of non-linear and ensemble-based models in handling complex feature interactions, affirming their suitability for accurate and robust breast cancer detection.

When Access Fails Quietly : A Privilege Maturity and Control Drift Framework for Governance Risk in Open-Source ERP Systems

Hussam Khalid Ahmed Mohammed * — 2025-08-28

Open-source ERP systems such as ERPNext provide flexibility for resource-constrained enterprises but often lack mature governance controls. This paper introduces a driftaware framework for access governance, centered on three novel constructs: the Privilege Maturity Index (PMI), Control Drift Taxonomy (CDT), and Access Governance Risk Score (AGRS). Validated through a longitudinal ERPNext case study in a Gulfbased firm, the model reveals how silent erosion of access discipline undermines governance integrity. Findings emphasize that systemic risks stem less from external breaches and more from organizational drift. In addition to highlighting an original framework, we show how the model naturally aligns with emerging guidance such as NIST CSF 2.0 and zero-trust architectures, ensuring both originality and applicability in modern governance contexts.

Early Prediction of Maternal Health Risks Using Machine Learning Techniques

Zayyanu Yunusa * — 2025-11-01

Accurate prediction of maternal health risks is critical for enabling early interventions to reduce pregnancy-related complications. This study evaluates six machine learning classifiers XGBoost, LightGBM, CatBoost, Random Forest, Gradient Boosting, and k-Nearest Neighbors (KNN) to classify risk levels using clinical parameters such as age, systolic diastolic blood pressure, blood glucose, heart rate, and body temperature. A rigorous feature importance analysis via Random Forest identified age and blood glucose levels as the most influential predictors, optimizing model efficiency. To ensure reliability, repeated stratified k-fold cross-validation was employed, minimizing bias and enhancing generalizability.

Among all classifiers, XGBoost achieved the highest accuracy 0.97%, demonstrating superior performance in risk stratification through its regularization and ensemble learning framework. In contrast, KNN recorded the lowest accuracy 0.94%, yet maintained clinical relevance due to its simplicity and interpretability. LightGBM, CatBoost, Random Forest, and Gradient Boosting also contributed robust results, further validating the efficacy of ensemble methods. The findings underscore the significance of feature selection, with age and blood glucose emerging as pivotal determinants of maternal risk. This study provides a scalable, data-driven framework for healthcare systems to prioritize high-risk pregnancies, offering actionable insights for timely interventions and informed clinical decision-making. By integrating these models into maternal care protocols, practitioners can enhance early diagnosis and reduce preventable morbidity, advancing equitable healthcare outcomes globally.

Design And Evaluation of A Language-Specific Keyboard Layout For Kanuri Language With Non-Standard Character

Idris Alhaji Adamu * — 2025-09-23

The Kanuri language, spoken by over 5 million people in West Africa, faces significant challenges in digital communication due to its non-standard characters and diacritics. This study designs, evaluates, and statistically validates a linguistically optimized Kanuri keyboard layout that outperforms QWERTY by 27.7 % in speed and 33.3 % in accuracy. Applying Fitts’ Law and character frequency analysis, the proposed layout prioritizes high-frequency characters (e.g., a, n, ə) on the home row and integrates dead keys for diacritics. Usability testing with 20 native speakers demonstrated a 27.7% improvement in typing speed (23.3 WPM vs. 18.1 WPM on QWERTY) and a 33.3% reduction in error rates. Iterative refinements based on user feedback resolved key accessibility issues, such as relocating the g key and optimizing modifier layers. The findings highlight the importance of language-specific keyboard designs in promoting digital inclusion and preserving linguistic diversity. This research contributes to human-computer interaction (HCI) by providing a replicable framework for marginalized languages with non-standard orthographies.

A Genetic Algorithm-Driven Personalized Genome Mutation Pathway Predictor for Early Diagnosis of Rare Polygenic Disorders

Akinrotimi Akinyemi Omololu * — 2025-09-18

Accurate prediction of rare polygenic disorders remains a significant chal-lenge in precision medicine, primarily due to the fact that they involve a complicated genetic architecture and current computational models are re-stricted. Traditional polygenic risk scores (PRS) have additive assumptions and finite cross-population validity and hence are not appropriate for rare disorders. In this study, a novel GA-based approach is presented that models individualized forward mutational routes, enabling early identification of risk genomic configurations. Each GA chromosome represents a binary vector of rare variants from whole-genome sequencing data, and evolutionary processes are guided by a composite fitness function. The function integrates pathogenicity scores, disease associations, and population rarity to yield biologically relevant simulations. Using 1000 Genomes Project data, we simulate 500 mutational trajectories in 500 different individuals. Results determine an average 27.2% increase in pathogenicity and 38.4% increase in harmful variants, with more than 60% convergence to known disease profiles in European and South Asian genomes. Approximately 24% of simulated genomes per individual exceed high-risk thresholds, outperforming PRS in identifying non-additive and epistatic effects. This GA strategy offers a dynamic, ancestry-aware approach to predicting rare disease risk, broadening the scope of predictive genomics and enabling earlier, more specific clinical interventions.