Integrated Dengue Prediction Dataset with Clinical Biomarkers, Weather Variables, and Risk Indicators (2023–2025)

Description

This dataset contains dengue-related clinical, environmental, and epidemiological records collected from January 2023 to 2025 for predictive analysis and machine learning research. The dataset integrates diagnostic biomarkers such as NS1, IgG, and IgM test results, platelet count, patient outcomes, and infection type along with environmental factors including rainfall, temperature, humidity, and lag-based weather indicators. Additionally, engineered features such as platelet risk, daily case count, case growth, vector index, and seasonal risk levels are included to support predictive modeling, disease surveillance, and public health analytics. This dataset is suitable for: Dengue prediction modeling Classification tasks Risk assessment analysis Public health research Climate-based disease forecasting Dataset format: XLSX Total records: 1500+ Features: 19 columns

Steps to reproduce

Download the file Dengue Dataset 2023-2025.xlsx. Open the dataset using Microsoft Excel, Python Pandas, or R. Perform data preprocessing by checking missing values and encoding categorical variables if needed. Split the dataset into training and testing sets for machine learning tasks. Use clinical and environmental features for dengue risk prediction, classification, or trend analysis. Evaluate model performance using accuracy, precision, recall, F1-score, or ROC-AUC.

Institutions

• Daffodil International University

  Dhaka Division, Dhaka

Categories

Licence