An Explainable Neuro-Fuzzy Model for Hypertension Prediction

This dataset consists of 1249 clinical records created for research on explainable machine learning and neuro-fuzzy modelling for hypertension prediction. The data set combines demographic, anthropometric, clinical, lifestyle and family history data that are relevant to hypertension risk classification. The data set includes 14 variables: Age, Gender, Height (cm), Weight (kg), Body Mass Index (BMI), Systolic Blood Pressure, Diastolic Blood Pressure, Cholesterol (mg/dL), Blood Glucose (mg/dL), Smoking, Alcohol Intake, Physical Activity, Family History, and Hypertension Status. The Hypertension_Status variable is the target variable for hypertension classification. The data set was prepared for the study “An Explainable Neuro-Fuzzy Model for Hypertension Prediction” at Osun State University, Osogbo, Nigeria. The accompanying research methodology is based on the selection of clinical variables for model development, which are Age, BMI, Systolic Blood Pressure, Diastolic Blood Pressure, Cholesterol Level, and Blood Glucose Level. The proposed modelling approach is a combination of fuzzy reasoning and machine learning to deal with the nonlinear relationships and uncertainty in clinical prediction. The research methodology involves data preprocessing, categorical encoding, feature selection, feature scaling, partitioning of the dataset, development of a neuro-fuzzy model, baseline machine learning modelling, performance evaluation, and explainability analysis using SHAP. The proposed neuro-fuzzy system is based on Sugeno-type fuzzy inference system with linguistic variables, membership functions and expert defined IF–THEN rules. The data set is designed to facilitate academic research, experimentation, benchmarking, machine learning development, explainable artificial intelligence research, and investigation of intelligent approaches for hypertension risk prediction. It is not intended to be used as a clinically validated diagnostic tool and predictions made by models trained on this data should not be used as a substitute for clinical assessment or clinical decision making.