Alzheimer's Disease Patient Data

What You Will Find in This Dataset:

1. Demographic Information:
   • Age: Patients’ ages range from 60 to 90 years, providing a diverse cohort for analyzing the age-relate progression of Alzheimer’s.
   • Gender: Gender is represent as 0 for Male and 1 for Female, allowing for gender-base analysis of disease prevalence and progression.
   • Ethnicity: The dataset includes ethnicity categories such as Caucasian, African American, Asian, and Other, facilitating the exploration of ethnic disparities in Alzheimer’s incidence and outcomes.
   • Education Level: Educational attainment is document, ranging from no formal education to advance degrees, which may be crucial in assessing cognitive resilience and the impact of education on disease progression.

2. Lifestyle Factors:

   • Body Mass Index (BMI): Each patient’s BMI, ranging from 15 to 40, is record, enabling analysis of the relationship between physical health and Alzheimer’s risk.
   • Smoking Status: The dataset notes whether the patient is a smoker (1 for Yes, 0 for No), considering smoking as a significant risk factor for cognitive decline.
   • Alcohol Consumption: Data on weekly alcohol consumption, ranging from 0 to 20 units, is include, providing insights into how alcohol affects cognitive health.
   • Physical Activity: Hours of physical activity per week, with values ranging from 0 to 10, are record to assess the impact of exercise on cognitive function and disease progression.
   • Diet Quality: Each patient is assign a diet quality score from 0 to 10, reflecting their overall nutritional habits, which may influence cognitive health and resilience.
   • Sleep Quality: Sleep quality is rate on a scale from 4 to 10, considering the significant role of sleep in cognitive function and overall brain health.

3. Medical History:

   • Family History of Alzheimer’s: The dataset records whether there is a family history of Alzheimer’s disease (1 for Yes, 0 for No), which is crucial for understanding genetic predispositions.
   • Cardiovascular Disease: The presence of cardiovascular disease is documented, linking heart health to brain health and its impact on Alzheimer’s risk.
   • Diabetes: Diabetes status is included, acknowledging its role in increasing.  The risk of cognitive impairment and Alzheimer’s.
   • Depression: The dataset includes information on whether the patient has experience depression. A known risk factor for cognitive decline.
   • Head Injury: Whether the patient has suffered a head injury is noted.  Given the strong correlation between traumatic brain injuries and an increase risk of Alzheimer’s.
   • Hypertension: High blood pressure status is recorded.  Emphasizing its significance in both cardiovascular and cognitive health.

Importance of the Dataset:

This dataset serves as a valuable resource for researchers, data scientists, and healthcare professionals focusing on Alzheimer’s disease. It offers a rich array of data that can be used to explore.  The various factors contributing to Alzheimer’s, develop predictive models, and investigate the influence of lifestyle, medical history. And cognitive assessments on the disease’s progression. By analyzing this data, researchers can gain critical insights into the development and progression of Alzheimer’s.  Potentially leading to new strategies for early detection, intervention, and management.