This dataset contains extensive health information for 2,149 patients, each uniquely identified with IDs ranging from 4751 to 6900. The dataset includes demographic details, lifestyle factors, medical history, clinical measurements, cognitive and functional assessments, symptoms, and a diagnosis of Alzheimer's Disease. The data is ideal for researchers and data scientists looking to explore factors associated with Alzheimer's, develop predictive models, and conduct statistical analyses.

Table of Contents

1. Patient Information
   • Patient ID
   • Demographic Details
   • Lifestyle Factors
2. Medical History
3. Clinical Measurements
4. Cognitive and Functional Assessments
5. Symptoms
6. Diagnosis Information
7. Confidential Information

Patient Information

Patient ID

• PatientID: A unique identifier assigned to each patient (4751 to 6900).

Demographic Details

• Age: The age of the patients ranges from 60 to 90 years.
• Gender: Gender of the patients, where 0 represents Male and 1 represents Female.
• Ethnicity: The ethnicity of the patients, coded as follows:
  • 0: Caucasian
  • 1: African American
  • 2: Asian
  • 3: Other
• EducationLevel: The education level of the patients, coded as follows:
  • 0: None
  • 1: High School
  • 2: Bachelor's
  • 3: Higher

Lifestyle Factors

• BMI: Body Mass Index of the patients, ranging 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 in hours, ranging from 0 to 10.
• DietQuality: Diet quality score, ranging from 0 to 10.
• SleepQuality: Sleep quality score, ranging from 4 to 10.

Medical History

• FamilyHistoryAlzheimers: Family history of Alzheimer's Disease, where 0 indicates No and 1 indicates Yes.
• CardiovascularDisease: Presence of cardiovascular disease, where 0 indicates No and 1 indicates Yes.
• Diabetes: Presence of diabetes, where 0 indicates No and 1 indicates Yes.
• Depression: Presence of depression, where 0 indicates No and 1 indicates Yes.
• HeadInjury: History of head injury, where 0 indicates No and 1 indicates Yes.
• Hypertension: Presence of hypertension, where 0 indicates No and 1 indicates Yes.

Clinical Measurements

• SystolicBP: Systolic blood pressure, ranging from 90 to 180 mmHg.
• DiastolicBP: Diastolic blood pressure, ranging from 60 to 120 mmHg.
• CholesterolTotal: Total cholesterol levels, ranging from 150 to 300 mg/dL.
• CholesterolLDL: Low-density lipoprotein cholesterol levels, ranging from 50 to 200 mg/dL.
• CholesterolHDL: High-density lipoprotein cholesterol levels, ranging from 20 to 100 mg/dL.
• CholesterolTriglycerides: Triglycerides levels, ranging from 50 to 400 mg/dL.

Cognitive and Functional Assessments

• MMSE: Mini-Mental State Examination score, ranging from 0 to 30. Lower scores indicate cognitive impairment.
• FunctionalAssessment: Functional assessment score, ranging from 0 to 10. Lower scores indicate greater impairment.
• MemoryComplaints: Presence of memory complaints, where 0 indicates No and 1 indicates Yes.
• BehavioralProblems: Presence of behavioral problems, where 0 indicates No and 1 indicates Yes.
• ADL: Activities of Daily Living score, ranging from 0 to 10. Lower scores indicate greater impairment.

Symptoms

• Confusion: Presence of confusion, where 0 indicates No and 1 indicates Yes.
• Disorientation: Presence of disorientation, where 0 indicates No and 1 indicates Yes.
• PersonalityChanges: Presence of personality changes, where 0 indicates No and 1 indicates Yes.
• DifficultyCompletingTasks: Presence of difficulty completing tasks, where 0 indicates No and 1 indicates Yes.
• Forgetfulness: Presence of forgetfulness, where 0 indicates No and 1 indicates Yes.

Diagnosis Information

• Diagnosis: Diagnosis status for Alzheimer's Disease, where 0 indicates No and 1 indicates Yes.

Confidential Information

• DoctorInCharge: This column contains confidential information about the doctor in charge, with "XXXConfid" as the value for all patients.

Conclusion

This dataset offers extensive insights into the factors associated with Alzheimer's Disease, including demographic, lifestyle, medical, cognitive, and functional variables. It is ideal for developing predictive models, conducting statistical analyses, and exploring the complex interplay of factors contributing to Alzheimer's Disease.

Citation

If you use this dataset in your work, please cite it as follows:

@misc{rabie_el_kharoua_2024,
  title={Alzheimer's Disease Dataset},
  url={https://www.kaggle.com/dsv/8668279},
  DOI={10.34740/KAGGLE/DSV/8668279},
  publisher={Kaggle...

The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) is a national genetics data repository facilitating access to genotypic and phenotypic data for Alzheimer's disease (AD). Data include GWAS, whole genome (WGS) and whole exome (WES), expression, RNA Seq, and CHIP Seq analyses. Data for the Alzheimer’s Disease Sequencing Project (ADSP) are available through a partnership with dbGaP (ADSP at dbGaP). Results are integrated and annotated in the searchable genomics database that also provides access to a variety of software packages, analytic pipelines, online resources, and web-based tools to facilitate analysis and interpretation of large-scale genomic data. Data are available as defined by the NIA Genomics of Alzheimer’s Disease Sharing Policy and the NIH Genomics Data Sharing Policy. Investigators return secondary analysis data to the database in keeping with the NIAGADS Data Distribution Agreement.

This dataset contains health records for 2,149 patients identified by unique ID numbers (4751-6900), providing extensive information on demographics, lifestyle, medical history, clinical measurements, cognitive and functional assessments, symptoms, and diagnostic data related to Alzheimer's disease.

2015-2022. This data set contains data from BRFSS.

The Oregon Alzheimer Disease Center is the core program of the Layton Aging & Alzheimer's Disease Center (LAADC), supported by the National Institute on Aging (NIA, NIH). We promote interactive, multidisciplinary research among the scientific community. Our primary emphasis is on studies of preclinical dementia, as well as early dementia. Well-characterized patients, clinical, MRI and genetic data, as well as biological specimens are made available to investigators and research groups worldwide.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fb75a86186a0014480c981c5182acc9ff%2Fgraph3.png?generation=1715898880551749&alt=media" alt="">this graph was created in Loocker studio,PowerBi,Tableau:

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Dementia patients show worsening cognitive function over time, beyond what might be expected from typical aging.

Dementia affects memory, thinking, orientation, comprehension, calculation, learning capacity, language, and judgment. This is commonly accompanied by changes in mood, emotional control, behavior, or motivation.

Deaths - Alzheimer's disease and other dementias - Sex: Both - Age: Age-standardized (Rate) Source Institute for Health Metrics and Evaluation, Global Burden of Disease (2019) – processed by Our World in Data Date range 1990–2019 Unit deaths per 100,000 people Links http://ghdx.healthdata.org/gbd-results-tool The data of this indicator is based on the following sources: Institute for Health Metrics and Evaluation, Global Burden of Disease (2019) Data published by Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2021.

Retrieved on September 22, 2021 Retrieved from http://ghdx.healthdata.org/gbd-results-tool How we process data at Our World in Data: All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline How to cite this data: In-line citation If you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Institute for Health Metrics and Evaluation, Global Burden of Disease (2019) – processed by Our World in Data

Full citation

Institute for Health Metrics and Evaluation, Global Burden of Disease (2019) – processed by Our World in Data. “Deaths - Alzheimer's disease and other dementias - Sex: Both - Age: Age-standardized (Rate)” [dataset]. Institute for Health Metrics and Evaluation, Global Burden of Disease (2019) [original data].

Organized by zipcode: Rates of Alzheimer's disease Percent of landcover types Modelled PM2.5 Socioeconomic variables. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Lucas Neas (CPHEA/PHESD/EB) is the owner of the copy of this dataset that was used. Format: Medicare database. This dataset is associated with the following publication: Wu, J., and L. Jackson. Greenspace inversely associated with the risk of Alzheimer’s disease in the mid-Atlantic United States. Earth. MDPI AG, Basel, SWITZERLAND, 2(1): 140-150, (2021).

The Critical Path for Alzheimer’s Disease (CPAD) data base contains patient-level data from 12,811 patients across 36 clinical trials of AD and MCI. All data has been remapped to a common data standard (CDISC SDTM v3.1.2) such that all the data can be analyzed across all studies. It is openly available to CPAD members, as well as to external qualified researchers who submit, and are approved for, a request for access. All data are fully de-identified.

Database of the results of the ADNI study. ADNI is an initiative to develop biomarker-based methods to detect and track the progression of Alzheimer's disease (AD) that provides access to qualified scientists to their database of imaging, clinical, genomic, and biomarker data.

Database that brings together funded Alzheimer's disease (AD) research supported by public and private organizations both in the US and abroad all categorized using the Common Alzheimer's Disease Research Ontology or CADRO. Launched as a joint collaboration between the National Institute on Aging (NIH) and the Alzheimer's Association, IADRP enables users the ability to assess the portfolios of major organizations (currently 30) for areas of overlap as well as areas of opportunities in which to collaborate and coordinate in a collective effort to advance AD research.

Source: CDC

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated 8/14/2024. Hospitalization Rate Related To Alzheimer's Or Other Dementias - This indicator shows the rate of hospitalizations related to Alzheimer's or other dementias (per 100,000 population). In the US, an estimated 5.4 million people are living with Alzheimer’s disease. Reducing the proportion of hospitalizations related to Alzheimer's and other dementias can decrease burdens on individuals, families, and the health care system in 2014.

The National Alzheimer’s Coordinating Center (NACC) was created in 1999 to facilitate research with data collected from Alzheimer's Disease Research Centers (ADRCs) across the United States. NACC oversees data collection and sharing for a number of datasets.

• The Uniform Data Set (UDS) contains longitudinal and standardized data collected from ADRCs since 2005. ADRC enrollment protocols vary and some ADRCs include volunteers with normal cognition. Each participant contributes data during annual visits to ADRCs, during which 16 data collection forms are completed with clinicians. In addition to the 16 forms, a subset of participants also complete the Frontotemporal Lobar Degeneration (FTLD) Module, the Lewy-body Dementia (LBD) Module, or the Down Syndrome (DS) Module. As of December 31, 2021, there are 44,713 subjects in the UDS.
• The Minimum Data Set (MDS) contains retrospectively compiled records of ADRC participants from 1984 to 2005 prior to the implementation of the UDS. The MDS does not contain detailed, longitudinal, or standardized clinical data.
• The Neuropathology (NP) Data Set includes autopsy data collected on UDS and MDS participants using standardized neuropathological evaluation forms.
• The Biomarker and Imaging Data Set includes MRI, PET, cerebrospinal fluid measurements (Abeta, P-tau, and T-tau), and limited genetic data (APOE genotype) contributed by a subset of UDS participants.

The DARWIN dataset includes handwriting data from 174 participants. The classification task consists in distinguishing Alzheimer’s disease patients from healthy people.

Creator: Francesco Fontanella

Source: https://archive.ics.uci.edu/dataset/732/darwin

The DARWIN dataset was created to allow researchers to improve the existing machine-learning methodologies for the prediction of Alzheimer's disease via handwriting analysis.

Citation Requests/Acknowledgements

N. D. Cilia, C. De Stefano, F. Fontanella, A. S. Di Freca, An experimental protocol to support cognitive impairment diagnosis by using handwriting analysis, Procedia Computer Science 141 (2018) 466–471. https://doi.org/10.1016/j.procs.2018.10.141

N. D. Cilia, G. De Gregorio, C. De Stefano, F. Fontanella, A. Marcelli, A. Parziale, Diagnosing Alzheimer’s disease from online handwriting: A novel dataset and performance benchmarking, Engineering Applications of Artificial Intelligence, Vol. 111 (20229) 104822. https://doi.org/10.1016/j.engappai.2022.104822

Data by medical encounter for the following conditions by age, race/ethnicity, and gender:
Alzheimer's Disease
Alzheimer's Disease and Related Dementias (ADRD)
Dementia
Neurocognitive Disorders
Parkinson's Disease

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.
Blank Cells: Rates not calculated for fewer than 11 events. Rates not calculated in cases where zip code is unknown. Geography not reported where there are no cases reported in a given year. SES: Is the median household income by SRA community. Data for SRAs only.
*The COVID-19 pandemic was associated with increases in all-cause mortality. COVID-19 deaths have affected the patterns of mortality, including those of ADRD conditions.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS). California Department of Health Care Access and Information (HCAI), Emergency Department Database and Patient Discharge Database, 2020. SANDAG Population Estimates, 2020 (vintage: 09/2022). Population estimates were derived using the 2010 Census and data should be considered preliminary. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, February 2023.

2020 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2020CommunityProfilesDataGuideandDataDictionaryDashboard_16763944288860/HomePage

IntroductionThis work presents a machine learning (ML) based risk prediction model for Alzheimer's disease and related dementias, utilizing real-world electronic health record (EHR) clinical data. While significant research has been conducted on dementia risk prediction, most studies rely on volunteer-based research cohorts rather than real-world clinical data. Using raw EHR data offers more realistic insights but poses challenges due to the extensive effort required to convert real-world EHR clinical data into a decision support system for daily clinical use.MethodsThe dataset consists of a high-volume, ten-year export of raw EHR data from Epic, the Johns Hopkins (JH) Health System. In this study, we utilized multimodal JH EHR data to develop a patient-based model to predict dementia onset over a five-year period. The interpretable binary classification model identified prognostic rulesets for dementia based on clinical characteristics.ResultsThe model achieved a mean test accuracy of 0.722 (95% CI: 0.722–0.723) and an AUROC of 0.795 (95% CI: 0.794–0.795) using 5-fold cross-validation across different sample subsets.DiscussionRecognizing that neurodegenerative diseases are often driven by multiple contributing factors rather than a single cause, we identify risk pathways by leveraging multimodal data and modeling their combined effects, leading to accurate dementia predictions and improved clinical interoperability.

ARWIBO is a cross-sectional dataset including data from more than 2,600 patients enrolled in Brescia and nearby areas. The database contains data of healthy elderly Controls (CTR), individuals with Mild Cognitive Impairment (MCI), and patients with Alzheimer's disease (AD). Images are both structural images weighted in T1 and T2 (at 1.0T or 1.5T) as well as a few PET scans.

Background Patients with Alzheimer's disease experience a progressive loss of cognitive function, and the ability to independently perform activities of daily life. Sometimes a dependent stage is reached quite early in the disease, when caregivers decide that the patients can no longer be left alone safely. This is an important aspect of Alzheimer's for patients, their families, and also health care providers. Understanding the relationship between a patient's current cognitive status and their need for care may assist clinicians when recommending an appropriate management plan. In this study, we investigated the relationship of cognitive function to dependence on caregivers before the patients reach a severe stage of the disease.

   Methods
   Data were obtained on 1,289 patients with mild-to-moderate Alzheimer's disease studied in two randomised clinical trials of galantamine (Reminyl®). Cognition was assessed using the cognitive part of the Alzheimer's Disease Assessment Scale (ADAS-cog) and Mini-Mental State Examination (MMSE). Patients were considered dependent if they required >12 hours of supervision each day or had high care needs. The Disability Assessment for Dementia (DAD) scale was also used as a measure of dependence. Disability was predicted directly using MMSE and ADAS-cog and compared to predictions from converted scores.


   Results
   The odds ratio of dependence was significantly higher amongst the patients with worse cognitive impairment, adjusting for age, gender and antipsychotic medication use. For example, a 4-point difference in ADAS-cog score was associated with an increase of 17% (95% CI 11–23) in the adjusted odds for >12 hours of supervision, and of 35% (95% CI 28–43) for dependence. Disability predicted directly using actual ADAS-cog and scores converted from MMSE values had close agreement using the models developed.


   Conclusion
   In patients with mild-to-moderate Alzheimer's disease, even relatively small degrees of poorer cognitive function increased the risk of losing the ability to live independently.

BackgroundAlzheimer’s disease and related dementias (ADRD) significant global public health challenges, leading to severe disability in patients and placing a heavy burden on caregivers. However, epidemiological studies focusing on ADRD in specific regions remain limited. This study aims to comprehensively analyze and describe the current status and changing trends of ADRD in Non-High-income East Asia (NHIEA), Non-High-income Southeast Asia (NHISEA), and High-income Asia Pacific (HIAP), providing more detailed real-world data to inform policymaking.MethodsThe data for ADRD used in this study were extracted from the 2021 Global Burden of Disease (GBD) database. We employed three major indicators of disease burden—prevalence, incidence, and years lived with disability (YLD)—and explored associated risk factors, further analyzing trends by age and sex. The results are presented as mean values with 95% uncertainty intervals (UIs). Additionally, we explored the differences between NHIEA, NHISEA, HIAP and other regions, as well as the potential associations between the disease burden of Alzheimer’s and other dementias and socioeconomic factors.ResultsThe findings indicate that the burden of dementia is rising in East and Southeast Asia, with women showing a higher burden across all indicators. Notably, in NHIEA, particularly in China, the burden of dementia has increased with the rising Social Demographic Index (SDI). China experienced a 27.3% increase in Alzheimer’s disease and other dementia ASYRs from 1990 to 2021, with a sharp 7.6% annual surge in 2021 alone, outpacing regional averages. Gender analysis revealed that women bear a disproportionate burden of Alzheimer’s disease and related dementias, especially after menopause, when the risk increases significantly. The study also identified smoking, high blood sugar, and high body mass index as important risk factors affecting the disease burden. The contribution of these risk factors varies across regions, genders, and age groups.ConclusionThe health burden of ADRD remains substantial, with distinct patterns observed across NHIEA, NHISEA, and HIAP, including regional variations in gender, age, and risk factors. These findings highlight the need for tailored approaches to allocate healthcare resources and implement appropriate control measures based on the specific conditions of each region to address this growing public health challenge. Future research should prioritize comparative analyses across continents and within regions to inform the development of more region-specific prevention strategies for ADRD.

The clinical, radiomics and genetic data to reproduce the key findings in "A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease".

Alzheimer’s disease is the most common cause of dementia. It is a neurodegenerative disorder characterized by gradually progressive cognitive and functional deficits, as well as behavioral changes. The diagnosis of Alzheimer’s disease is often challenging leading to suboptimal patient care. In this study, we develop a new unsupervised analytic method based on the extraction of statistical features from multiple brain regions identified through structural magnetic resonance imaging data, which is able to reliably discriminate people with Alzheimer’s disease-related pathologies from those without. We provide a diagnostic tool that is ready to be integrated into the clinical decision support system without the need for additional sampling or patient testing.