IPESS is a free, full-text archive of peer-reviewed journals and conference proceedings from Joseph Sarwuan Tarka University. Discover and publish — freely, openly, for every discipline.
African Journal of Health Informatics
Machine Learning Approaches for Early Detection of Malaria in Sub-Saharan Africa
This study investigates the application of supervised machine learning algorithms — including Random Forest, Support Vector Machines, and Gradient Boosting — to the early diagnosis of malaria using clinical and haematological data collected from tertiary hospitals across Sub-Saharan Africa. A dataset of 14,200 patient records was used for training and evaluation. The proposed ensemble model achieved a sensitivity of 94.3% and specificity of 91.7%, outperforming conventional microscopy-based screening in resource-constrained settings. The findings suggest that low-cost, deployable ML pipelines can significantly reduce diagnostic delays and improve patient outcomes in endemic regions.
From registration to global audience — it takes minutes to start and costs nothing.
Register with your university or institutional email. Complete your researcher profile in under five minutes.
Upload your manuscript, add co-authors, keywords, and abstract. Our system guides you at every step.
Expert reviewers in your field assess your work and provide structured, constructive feedback.
Your paper goes live and becomes discoverable to researchers worldwide across all major indexes.
No publication fees · Permanently open access · Email verification required
Peer-reviewed research from our global scholarly community
African Journal of Health Informatics
This study investigates the application of supervised machine learning algorithms — including Random Forest, Support Vector Machines, and Gradient Boosting — to the early diagnosis of malaria using clinical and haematological data collected from tertiary hospitals across Sub-Saharan Africa. A dataset of 14,200 patient records was used for training and evaluation. The proposed ensemble model achieved a sensitivity of 94.3% and specificity of 91.7%, outperforming conventional microscopy-based screening in resource-constrained settings. The findings suggest that low-cost, deployable ML pipelines can significantly reduce diagnostic delays and improve patient outcomes in endemic regions.
Nigerian Journal of Technology and Innovation
Academic credential fraud remains a persistent challenge in Nigeria, with significant consequences for institutional trust and graduate employability. This paper proposes a permissioned blockchain architecture — built on Hyperledger Fabric — to enable tamper-proof issuance and real-time verification of academic certificates. A prototype was deployed in collaboration with three Nigerian universities and evaluated across 2,400 credential verification requests. The system achieved near-instant verification (mean latency: 1.2 s) with zero false positives, compared to a mean turnaround of 11 business days for conventional verification channels. Policy implications for national adoption are discussed.
Browse thousands of peer-reviewed articles and conference papers from researchers worldwide — completely free to read and download.
Browse Publications →Join researchers already publishing on IPESS. No fees, no barriers — just rigorous peer review and global visibility.
Create Free Account →Supported By
Explore research across popular subject areas