SEPSIS HETEROGENOUS CLINICAL DATASET

This dataset consists of 1,700 clinical records created for research on machine learning based early prediction of sepsis from heterogeneous clinical data. The data set includes nine clinical variables that represent vital signs and laboratory measurements, and a binary target variable that represents the presence or absence of sepsis. The predictor variables are Heart Rate, Temperature, Systolic Blood Pressure, Diastolic Blood Pressure, Respiratory Rate, Oxygen Saturation, White Blood Cell (WBC) Count, Lactate Level (mmol/L), and Platelet Count. The target variable Sepsis_Flag is a binary variable that is 0 if the patient does not have sepsis and 1 if the patient has sepsis. There are 1050 records classified as non-sepsis (61.76%) and 650 records classified as sepsis (38.24%). There are missing observations in some clinical variables, especially in the case of Lactate Level, Platelet Count, and WBC Count. The characteristics of this dataset make it appropriate for studying clinical data preprocessing, missing value imputation, class imbalance, feature selection, and supervised machine learning methods for sepsis prediction. The data set was created for the project “Machine Learning Based Early Prediction of Sepsis Using Heterogeneous Clinical Data” at Osun State University, Osogbo, Nigeria. It is designed to enable academic research, experimentation, benchmarking, and development of machine learning models for early sepsis risk prediction. The data set should be used for research and education. It is not intended to be used as a clinically validated diagnostic tool and predictions made using models trained on this data should not replace clinical assessment or clinical decision making. Users of this dataset are requested to cite the dataset and acknowledge the contributors and institution appropriately.