Predictive Clinical Dataset for Dengue Fever Using Vital Signs and Blood Parameters
Description
Dengue fever remains a serious public health issue in Bangladesh, particularly in regions like Kalai, Joypurhat, where favorable conditions lead to regular outbreaks. The lack of early diagnostic tools and the limited availability of healthcare infrastructure amplify the severity of these outbreaks. In light of these challenges, a comprehensive clinical and hematological dataset has been developed to support the prediction of dengue fever, aiming to assist in early diagnosis and improve patient outcomes. Dataset Overview: This dataset includes vital signs and hematological parameters from patients diagnosed with dengue fever. The data aims to support the creation of predictive models that use machine learning algorithms to estimate the likelihood and severity of dengue based on clinical parameters. The dataset is ideal for researchers, healthcare professionals, and public health officials who are working to improve diagnosis, treatment, and epidemiological management of dengue fever. Clinical Parameters Included: Age: Patient’s age in years. Sex: Gender of the patient (Male/Female). Hemoglobin (g/dL): Hemoglobin levels in the blood. WBC Count (x10^3/µL): Total white blood cell count. Differential Count (%): Distribution of different types of white blood cells. RBC Panel: Detailed red blood cell measurements, including counts and morphology. Platelet Count (x10^3/µL): Total number of platelets in the blood. PDW (%): Platelet distribution width, which indicates variability in platelet size. Data Collection: The dataset was compiled from patients at the Upazila Health Complex in Kalai, Joypurhat. All data collection procedures followed ethical guidelines, with patient information anonymized to protect confidentiality. Structure of the Dataset: Format: CSV Rows: 1,003 (individual patient records) Columns: 9 (clinical parameters)