IoT-Integrated Smart Ward Dataset for Environmental, Physiological, and Clinical Analysis of Patient Recovery

The IoT-Integrated Smart Ward Dataset: Environmental, Physiological, and Clinical Insights into Patient Recovery contains information from 688 inpatients admitted to a digitally monitored hospital in Pabna, Bangladesh, between April and October 2025. The dataset combines patient demographics, IoT-sensed ward conditions, physiological data, and recovery outcomes to explore how smart ward environments influence patient recovery.

It includes 21 features grouped into four categories:
- Demographics: Patient_ID, Age, Gender, Blood_Group

- Environment (IoT Sensors): Room_Temperature (°C), Humidity (%), Noise_Level (dB), Air_Quality_Index (AQI), Light_Intensity (Lux)

- Physiological & Clinical: Heart_Rate (bpm), Blood_Pressure (mmHg), Body_Temperature (°C), Respiration_Rate (bpm), Oxygen_Saturation (SpO₂ %), Pain_Level_Score (1–10), Sleep_Quality (1–10), Primary_Diagnosis, Severity_Level

- Treatment & Outcome: Treatment_Type, Ward_Type, Recovery_Speed (days)

Data were collected from hospital IoT systems and electronic medical records, then carefully cleaned and standardized to ensure quality and consistency. Missing values were imputed, and all patient identifiers were anonymized for ethical use. This dataset serves as a valuable benchmark for data-driven healthcare research, enabling analysis of environmental, physiological, and clinical factors in patient recovery through machine learning and statistical modeling.