This synthetic Alzheimer's Disease Dataset is designed for educational and research purposes in the fields of data science, healthcare, and public health. The dataset contains essential features such as age, gender, medical history, lifestyle factors, and cognitive assessments to study and predict the onset and progression of Alzheimer's disease.

Dataset Features

• patient_id: Unique identifier for each individual in the dataset.
• age: Age of the individual.
• gender: Gender of the individual (e.g., Male, Female).
• ethnicity: Ethnic background of the individual (e.g., Caucasian, Asian).
• education_level: Highest level of education attained by the individual (e.g., High School, Bachelor's).
• bmi: Body Mass Index (BMI) of the individual.
• smoking: Whether the individual is a smoker ("Yes" or "No").
• alcohol_consumption: Frequency of alcohol consumption (e.g., daily, weekly).
• physical_activity: Level of physical activity (numeric value).
• diet_quality: Quality of the individual's diet (numeric value).
• sleep_quality: Quality of the individual's sleep (numeric value).
• family_history_alzheimers: Whether the individual has a family history of Alzheimer's disease.
• cardiovascular_disease: Whether the individual has a history of cardiovascular disease.
• diabetes: Whether the individual has diabetes.
• depression: Whether the individual has a history of depression.
• head_injury: Whether the individual has had a head injury.
• hypertension: Whether the individual has hypertension.
• systolic_bp: Systolic blood pressure value.
• diastolic_bp: Diastolic blood pressure value.
• cholesterol_total: Total cholesterol level.
• cholesterol_ldl: Low-density lipoprotein (LDL) cholesterol level.
• cholesterol_hdl: High-density lipoprotein (HDL) cholesterol level.
• cholesterol_triglycerides: Triglycerides level in the blood.
• mmse: Mini-Mental State Examination score (used to assess cognitive function).
• functional_assessment: A functional assessment score.
• memory_complaints: Whether the individual has memory complaints ("Yes" or "No").
• behavioral_problems: Whether the individual has behavioral problems ("Yes" or "No").
• adl: Activities of daily living score (indicating the individual’s ability to perform daily tasks).
• confusion: Whether the individual experiences confusion ("Yes" or "No").
• disorientation: Whether the individual experiences disorientation ("Yes" or "No").
• personality_changes: Whether there are noticeable personality changes ("Yes" or "No").
• difficulty_completing_tasks: Whether the individual has difficulty completing tasks ("Yes" or "No").
• forgetfulness: Whether the individual experiences forgetfulness ("Yes" or "No").
• diagnosis: The final diagnosis of the individual, indicating whether they have Alzheimer's disease or not.

Distribution

Usage

This dataset is ideal for a range of applications:

• Alzheimer's Disease Prediction: Build machine learning models to predict the likelihood of Alzheimer's disease based on contributing factors.
• Risk Factor Analysis: Analyze which factors (e.g., medical conditions, lifestyle choices) are associated with higher risks of developing Alzheimer's disease.
• Cognitive Function Analysis: Study the relationship between cognitive scores (e.g., MMSE) and health metrics.
• Personalized Care Planning: Use insights from the data to guide healthcare professionals in developing tailored care plans for Alzheimer's patients.
• Public Health Research: Investigate trends and correlations in Alzheimer’s disease prevalence across different demographics and lifestyle factors.

License

CC0

Who Can Use It

• Data Science Practitioners: For practising data preprocessing, classification, and regression tasks.
• Healthcare Researchers: To explore the relationships between lifestyle factors, medical conditions, and cognitive decline.
• Public Health Analysts: To analyze Alzheimer's disease prevalence and risk factors in different populations.
• Healthcare Professionals: For guiding diagnosis and treatment plans based on data-driven insights.
• Policy Makers: To make informed decisions about Alzheimer's disease prevention and care.