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Author/Affiliation: Peter Adebayo Odesola
A Step Toward the Use of AI for Polyendocrine Metabolic Ovarian Syndrome (PMOS): Cost-Aware Risk Stratification with UncertaintyAware Triage and Conformal Prediction
by Adewale Alex Adegoke, Idris Babalola and Peter Adebayo Odesola
Journal of Engineering Research and Sciences, Volume 5, Issue 8, Page # 1-27, 2026; DOI: 10.55708/js0508001
Abstract: Polyendocrine metabolic ovarian syndrome (PMOS) previously known as Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age, yet its diagnosis remains challenging because accurate assessment often depends on investigations that are costly, less accessible, and difficult to scale in resource-constrained clinical settings. We develop and assess a cost-aware modelling framework… Read More
(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))
Model Uncertainty Quantification: A Post Hoc Calibration Approach for Heart Disease Prediction
by Peter Adebayo Odesola, Adewale Alex Adegoke and Idris Babalola
Journal of Engineering Research and Sciences, Volume 4, Issue 12, Page # 25-54, 2025; DOI: 10.55708/js0412003
Abstract: We investigated whether post-hoc calibration improves the trustworthiness of heart-disease risk predictions beyond discrimination metrics. Using a Kaggle heart-disease dataset (n = 1,025), we created a stratified 70/30 train-test split and evaluated six classifiers, Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Naive Bayes, Random Forest, and XGBoost. Discrimination was quantified by stratified 5-fold cross-validation with… Read More
(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))