Sose007/hypertension-risk-prediction-nigeria

Using data and risk factors to identify Nigerians at high risk for hypertension or its deadly complications, enabling early, targeted prevention. # hypertension-risk-prediction-nigeria ML project to predict hypertension risk using WHO survey data # Predicting Hypertension Risk Using WHO NCD Survey Data (Nigeria) 👨‍⚕️ By Dr. Godswill Eromosele 🔬 AI & Machine Learning in Medicine | Internal Medicine | Public Health ## 📌 Project Overview This project uses data from the WHO STEPS survey in Nigeria to build a basic machine learning model that predicts hypertension risk based on common demographic and clinical features like age, BMI, smoking history, and blood pressure. ## 🎯 Objective - Train a logistic regression model to classify individuals as at risk or not at risk of hypertension. - Learn and demonstrate foundational data science skills in healthcare. - Contribute to local solutions using open data and simple AI tools. ## 🧰 Tools & Libraries - Google Colab - Python (Pandas, Matplotlib, Seaborn, Scikit-learn) - Dataset: WHO STEPS NCD Risk Factor Survey (Nigeria) ## 📊 Key Features Used - Age - Gender - BMI - Systolic/Diastolic BP - Smoking & alcohol history - Diabetes diagnosis ## ✅ Model Used - Logistic Regression Evaluation: Confusion matrix, accuracy, precision/recall ## 📎 Next Steps - Deploy as a public health tool or triage support - Expand model with more clinical data - Write a blog post/LinkedIn article explaining the process --- 🧠 **Inspired by the goal of leveraging AI to improve healthcare outcomes in Nigeria and globally.**