Dataset for IoT-Enabled Smart Ward Environments Supporting Patient Recovery

Published: 17 October 2025| Version 2 | DOI: 10.17632/rv36nm4wy8.2
Contributors:
, Md. Rokibul Islam Likhon,
,
,
,
, Md. Jakir Hossen, Firoz Mridha

Description

The Dataset for IoT-Enabled Smart Ward Environments Supporting Patient Recovery contains information from 928 inpatients admitted to the Digital Hospital in Pabna, Bangladesh, between April and August 2025. This dataset combines patient demographic information, ward environmental conditions recorded by IoT sensors, physiological measurements, and recovery outcomes to study how smart ward factors affect patient health and recovery. The dataset includes 12 key features: - Demographics: Patient_ID, Age, Gender - Environment (IoT sensors): Room_Temperature (°C), Humidity (%), Noise_Level (dB), Air_Quality_Index (AQI), Light_Intensity (Lux) - Physiological: Heart_Rate (bpm), Blood_Pressure (mmHg), Sleep_Quality (scale 1–5) - Outcome: Recovery_Speed (days to discharge) Data for this dataset was collected from IoT devices and hospital records, then carefully preprocessed to remove duplicates, standardize units, and handle missing values. It enables the study of how ward conditions affect patient vitals, sleep quality, and recovery speed, supporting research in machine learning, deep learning, and statistical modeling. Potential applications include predicting recovery speed, assessing patient risks, optimizing ward environments, and designing smart hospital management systems, offering valuable insights for enhancing patient care and advancing digital health innovation.

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Institutions

  • Trine University
  • American International University Bangladesh
  • Southeast University
  • Khulna University of Engineering and Technology
  • Multimedia University

Categories

Machine Learning, Medical Care, Health Care Environment in Health Care System, IoT Sensor

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