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Asthma Risk Factor Dataset
Patient Health Records & Digital Health
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About
This dataset contains extensive health information for patients diagnosed with Asthma Disease. It serves as a valuable resource for researchers and data scientists aiming to explore factors associated with Asthma, develop predictive models, and conduct statistical analyses. This original, synthetic dataset was generated specifically for educational purposes, making it ideal for various data science and machine learning projects. It offers significant insights into the interplay of factors contributing to Asthma Disease.
Columns
- Patient ID: A unique identifier assigned to each patient, ranging from 5034 to 7425.
- Age: The age of the patients, spanning from 5 to 80 years.
- Gender: Patient's gender, where 0 represents Male and 1 represents Female.
- Ethnicity: Patient's ethnicity, coded as 0 for Caucasian, 1 for African American, 2 for Asian, and 3 for Other.
- EducationLevel: Patient's education level, coded as 0 for None, 1 for High School, 2 for Bachelor's, and 3 for Higher.
- BMI: Body Mass Index of the patients, ranging from 15 to 40.
- Smoking: Smoking status, with 0 indicating No and 1 indicating Yes.
- PhysicalActivity: Weekly physical activity in hours, ranging from 0 to 10.
- DietQuality: A score representing diet quality, ranging from 0 to 10.
- SleepQuality: A score indicating sleep quality, ranging from 4 to 10.
- PollutionExposure: A score for exposure to pollution, from 0 to 10.
- PollenExposure: A score for exposure to pollen, from 0 to 10.
- DustExposure: A score for exposure to dust, from 0 to 10.
- PetAllergy: Pet allergy status, where 0 indicates No and 1 indicates Yes.
- FamilyHistoryAsthma: Family history of asthma, where 0 indicates No and 1 indicates Yes.
- HistoryOfAllergies: History of allergies, where 0 indicates No and 1 indicates Yes.
- Eczema: Presence of eczema, where 0 indicates No and 1 indicates Yes.
- HayFever: Presence of hay fever, where 0 indicates No and 1 indicates Yes.
- GastroesophagealReflux: Presence of gastroesophageal reflux, where 0 indicates No and 1 indicates Yes.
- LungFunctionFEV1: Forced Expiratory Volume in 1 second (FEV1), ranging from 1.0 to 4.0 litres.
- LungFunctionFVC: Forced Vital Capacity (FVC), ranging from 1.5 to 6.0 litres.
- Wheezing: Presence of wheezing, where 0 indicates No and 1 indicates Yes.
- ShortnessOfBreath: Presence of shortness of breath, where 0 indicates No and 1 indicates Yes.
- ChestTightness: Presence of chest tightness, where 0 indicates No and 1 indicates Yes.
- Coughing: Presence of coughing, where 0 indicates No and 1 indicates Yes.
- NighttimeSymptoms: Presence of nighttime symptoms, where 0 indicates No and 1 indicates Yes.
- ExerciseInduced: Presence of symptoms induced by exercise, where 0 indicates No and 1 indicates Yes.
- Diagnosis: Diagnosis status for Asthma, where 0 indicates No and 1 indicates Yes.
- DoctorInCharge: This column contains confidential information, with "Dr_Confid" as the value for all patients.
Distribution
The dataset is provided in a tabular format, likely as a CSV file (e.g.,
asthma_disease_data.csv). It contains health information for 2,392 patients, with no missing values across its 29 columns.Usage
This dataset is ideal for:
- Researchers and data scientists aiming to explore factors associated with Asthma.
- Developing predictive models for Asthma diagnosis.
- Conducting statistical analyses on asthma-related health data.
- Educational purposes in data science and machine learning projects.
Coverage
The dataset covers patient demographic details including age (5 to 80 years), gender (Male/Female), ethnicity (Caucasian, African American, Asian, Other), and education level (None, High School, Bachelor's, Higher). It also includes lifestyle, environmental, allergy, medical history, clinical measurements, and symptom data. Specific geographic or time range information is not available in the provided details.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Data Scientists: For building machine learning models to predict Asthma or identify key risk factors.
- Medical Researchers: To study the correlation between lifestyle, environmental factors, and Asthma prevalence.
- Students and Educators: For learning and teaching data analysis, statistical modelling, and machine learning concepts using a real-world health scenario.
- Public Health Analysts: To understand demographic and lifestyle patterns associated with Asthma.
Dataset Name Suggestions
- Asthma Patient Health Data
- Asthma Risk Factor Dataset
- Synthetic Asthma Patient Study
- Asthma Disease Patient Records
Attributes
Original Data Source:Asthma Risk Factor Dataset
QUALITY