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Patient Alzheimers Predictor Data
Patient Health Records & Digital Health
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About
This dataset provides detailed health information for over 2,100 patients, specifically curated for the study of Alzheimer's Disease. It brings together a variety of patient attributes including demographic details, lifestyle choices, medical history, clinical measurements, and various cognitive and functional assessments. The dataset also records the presence of common symptoms associated with Alzheimer's and a final diagnosis status. It is an ideal resource for researchers and data scientists aiming to explore factors linked to Alzheimer's, develop predictive models, and perform statistical analyses in the field of health. It is particularly suitable for data science and machine learning projects focused on understanding and predicting Alzheimer's Disease.
Columns
- PatientID: A unique numerical identifier for each patient, ranging from 4751 to 6900.
- Age: The patient's age in years, between 60 and 90.
- Gender: Coded as 0 for Male and 1 for Female.
- Ethnicity: Coded as 0 for Caucasian, 1 for African American, 2 for Asian, and 3 for Other.
- EducationLevel: Coded as 0 for None, 1 for High School, 2 for Bachelor's, and 3 for Higher education.
- BMI: Body Mass Index, with values from 15 to 40.
- Smoking: Smoking status, where 0 indicates No and 1 indicates Yes.
- AlcoholConsumption: Weekly alcohol consumption in units, ranging from 0 to 20.
- PhysicalActivity: Weekly physical activity measured in hours, from 0 to 10.
- DietQuality: A score indicating diet quality, from 0 to 10.
- SleepQuality: A score reflecting sleep quality, from 4 to 10.
- FamilyHistoryAlzheimers: Indicates a family history of Alzheimer's Disease, with 0 for No and 1 for Yes.
- CardiovascularDisease: Presence of cardiovascular disease, with 0 for No and 1 for Yes.
- Diabetes: Presence of diabetes, with 0 for No and 1 for Yes.
- Depression: Presence of depression, with 0 for No and 1 for Yes.
- HeadInjury: History of head injury, with 0 for No and 1 for Yes.
- Hypertension: Presence of hypertension, with 0 for No and 1 for Yes.
- SystolicBP: Systolic blood pressure, ranging from 90 to 180 mmHg.
- DiastolicBP: Diastolic blood pressure, ranging from 60 to 120 mmHg.
- CholesterolTotal: Total cholesterol levels, from 150 to 300 mg/dL.
- CholesterolLDL: Low-density lipoprotein cholesterol levels, from 50 to 200 mg/dL.
- CholesterolHDL: High-density lipoprotein cholesterol levels, from 20 to 100 mg/dL.
- CholesterolTriglycerides: Triglycerides levels, from 50 to 400 mg/dL.
- MMSE: Mini-Mental State Examination score, from 0 to 30, where lower scores suggest cognitive impairment.
- FunctionalAssessment: Functional assessment score, from 0 to 10, with lower scores indicating greater impairment.
- MemoryComplaints: Presence of memory complaints, 0 for No and 1 for Yes.
- BehavioralProblems: Presence of behavioural problems, 0 for No and 1 for Yes.
- ADL: Activities of Daily Living score, from 0 to 10, where lower scores suggest greater impairment in daily tasks.
- Confusion: Presence of confusion, 0 for No and 1 for Yes.
- Disorientation: Presence of disorientation, 0 for No and 1 for Yes.
- PersonalityChanges: Presence of personality changes, 0 for No and 1 for Yes.
- DifficultyCompletingTasks: Presence of difficulty completing tasks, 0 for No and 1 for Yes.
- Forgetfulness: Presence of forgetfulness, 0 for No and 1 for Yes.
- Diagnosis: Diagnosis status for Alzheimer's Disease, with 0 for No and 1 for Yes.
- DoctorInCharge: A confidential column, consistently showing "XXXConfid" as its value.
Distribution
The dataset is provided in a tabular format, likely as a CSV file (
alzheimers_disease_data.csv). It contains data for 2,149 patients and includes 35 columns, occupying approximately 605.25 KB of space. All columns have valid, non-missing, and non-mismatched data, indicating a clean and ready-to-use structure.Usage
This dataset is ideal for:
- Exploring factors associated with Alzheimer's Disease.
- Developing predictive models for Alzheimer's diagnosis or progression.
- Conducting statistical analyses on various health and lifestyle factors linked to the disease.
- Investigating the intricate relationships between demographic, lifestyle, medical, cognitive, and functional variables.
- General data science and machine learning projects in the health domain.
Coverage
The dataset includes demographic information such as age (patients aged 60 to 90 years), gender, ethnicity (Caucasian, African American, Asian, Other), and education level. It does not specify geographic coverage or a particular time range for data collection.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
This dataset is primarily intended for:
- Researchers: To explore and understand the various factors contributing to Alzheimer's Disease.
- Data Scientists: For building and testing predictive models, and conducting in-depth statistical analyses.
- Academics: For educational purposes and machine learning projects focusing on health conditions.
Dataset Name Suggestions
- Alzheimer's Patient Health Data
- Patient Alzheimer's Predictor Data
- Cognitive Health Factors Dataset
- Elderly Health & Alzheimer's Data
- AD Patient Medical Records
Attributes
Original Data Source: Patient Alzheimers Predictor Data
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