Skip to content

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Jul 2026

Community-Based Screening for Undiagnosed Hyperglycemia and Prediabetes in Southeastern Nigeria

Abstract Objectives This study aimed to determine the prevalence of abnormal fasting blood glucose levels and identify associated risk factors among adults in a rural community in southeastern Nigeria. Materials and Methods A community-based, cross-sectional screening study was conducted among 200 adults aged ≥18 years in Okija, Anambra State, Nigeria. Participants were recruited through voluntary participation during a community health outreach program. Fasting blood glucose was measured using capillary blood obtained via finger prick with a calibrated portable glucometer following overnight fasting. Anthropometric indices, including body mass index (BMI) and waist circumference, were recorded. Demographic and lifestyle data were collected using a structured questionnaire. Results Twenty-four participants (12.0%) had fasting blood glucose levels consistent with possible diabetes, while 59 (29.5%) had impaired fasting glucose (prediabetes), with 83 (41.5%) participants exhibiting abnormal glucose regulation. Elevated fasting blood glucose levels were significantly associated with age group (χ 2  = 17.61, df = 7, p  = 0.020), waist circumference (χ 2  = 4.79, df = 1, p  = 0.029), carbohydrate intake (χ 2  = 4.50, df = 1, p  = 0.034), and fat intake (χ 2  = 6.14, df = 1, p  = 0.013). Conclusion There is a high burden of abnormal fasting blood glucose levels and prediabetes in this rural population. Community-based screening and targeted lifestyle interventions are essential to reduce the growing burden of diabetes.

C. Nweke, Nmasichukwu A. Anazodo, O. C. Iloka et al. · 0 citations
Open access Aug 2026

Development of a Random Forest-Based Predictive Model for Polycystic Ovary Syndrome (PCOS) Using SHAP for Explanability

Polycystic Ovary Syndrome (PCOS) is a lead major disorder and primary cause of infertility in women of reproductive age, affecting about 13% of this population globally with over 70% of cases remaining undiagnosed. Early diagnosis is yet challenging, particularly in low-resource settings where ultrasound imaging is inaccessible. This study focuses on leveraging Random Forest (RF) model for PCOS prediction using only clinical and biochemical data, enhanced with Shapley Additive Explanations (SHAP) for model interpretability. A publicly available Kaggle PCOS dataset from 541 women (177 PCOS-positive, 364 negative) across 10 hospitals in Kerala, India, was utilised. A leakage-free preprocessing pipeline applied median and mode imputation before data splitting.

C. Nweke, Prema Kirubakaran, Ridwan Kolapo · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.