Maladie

## Overview

Maladie is a dataset for instance segmentation tasks - it contains Ble annotations for 300 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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},
  author={Rabie El Kharoua},
  year={2024}
}

Dataset Usage and Attribution Notice

This dataset, shared by Rabie El Kharoua, is original and has never been shared before. It is made available under the CC BY 4.0 license, allowing anyone to use the dataset in any form as long as proper citation is given to the author. A DOI is provided for proper referencing. Please note that duplication of this work within Kaggle is not permitted.

Exclusive Synthetic Dataset

This dataset is synthetic and was generated for educational purposes, making it ideal for data science and machine learning projects. It is an original dataset, owned by Mr. Rabie El Kharoua, and has not been previously shared. You are free to use it under the license outlined on the data card. The dataset is offered without any guarantees. Details about the data provider will be shared soon.

Ce jeu de données est une des sources de datavisualisations disponibles sur le site Data pathologies.

Mise à jour du 01/07/2024 : actualisation des données de 2015 à 2022

L’historique des données a été mis à jour :

les données de 2015 à 2021 ont été actualisées les données de 2022 sont maintenant disponibles

Cette nouvelle version des données (juillet 2024) remplace ainsi la précédente (datant de juillet 2023).

Informations générales :

Les données présentent des informations sur les dépenses remboursées par l'ensemble des régimes d'assurance maladie et affectées à chaque pathologie, traitement chronique ou épisode de soins. Ces dépenses sont réparties entre :

les soins de ville (dont soins de médecins, soins infirmiers ou de kinésithérapie, médicament, biologie, transports, etc.) ;

les hospitalisations dans des établissements de santé publics ou privés ; les prestations en espèces (dont indemnités journalières).

Les pathologies, traitements chroniques et épisodes de soins sont regroupés dans les catégories suivantes :

maladies cardioneurovasculaires ; traitements du risque vasculaire (hors pathologies) ; diabète ; cancers ; pathologies psychiatriques ; traitements psychotropes (hors pathologies) ; maladies neurologiques ; maladies respiratoires chroniques (hors mucoviscidose) ; maladies inflammatoires ou rares ou infection VIH ; insuffisance rénale chronique terminale ; maladies du foie ou du pancréas (hors mucoviscidose) ; affections de longue durée (dont 31 et 32) pour d'autres causes ; hospitalisation pour Covid-19 (à partir de 2020) ; maternité (avec ou sans pathologies) ; traitement antalgique ou anti-inflammatoire (hors pathologies, traitements, maternité ou hospitalisations) ; hospitalisations hors pathologies repérées (avec ou sans pathologies, traitements ou maternité).

Les dépenses remboursées des personnes qui ne présentent aucune des pathologies, traitements chroniques ou épisodes de soins mentionnés ci-dessus sont également présentées, ainsi que celles attribuées à la consommation courante de soins pour l’ensemble de la population de la cartographie des pathologies et des dépenses de l'Assurance Maladie.

Les dépenses proposées sont de différents types :

dépenses totales annuelles remboursées (par an) ; dépenses moyennes annuelles remboursées (par an et par patient).

Les effectifs de patients sont également présentés dans les données. La population de référence est celle de la cartographie des pathologies et des dépenses de l'Assurance Maladie.

Qu’est-ce que la population de la cartographie des pathologies et des dépenses de l’Assurance Maladie ?

La population de la cartographie recense l’ensemble des bénéficiaires de l’assurance maladie obligatoire, quel que soit leur régime d’affiliation :

ayant bénéficié d'au moins une prestation dans l’année (soins de médecins, soins infirmiers ou de kinésithérapie, médicament, biologie, transports etc.) ; et/ou ayant séjourné au moins une fois dans un établissement de santé public ou privé dans l’année (séjours en médecine, chirurgie, obstétrique, psychiatrie, soins de suite et de réadaptation, actes et consultations externes ou hospitalisation à domicile).

Elle rassemble en 2022, 68,7 millions de bénéficiaires de l'ensemble des régimes d'assurance maladie, ayant eu recours à des soins remboursés. Cette population est utilisée par la Caisse nationale de l’assurance Maladie (Cnam) pour réaliser de nombreuses études et produire des données sur les pathologies et les dépenses de l’Assurance Maladie. Pour plus d'informations, consulter la page Méthode de ce site.

Secret statistique : Par respect pour le secret statistique (loi du 7 juin 1951) et afin que l’identification directe ou indirecte des individus soit impossible, aucune information sur les effectifs n'est communiquée lorsque le nombre de patients pris en charge est inférieur à 11. La valeur de l'indicateur est alors indiqué par la variable « NS » (non significatif) dans le jeu de données et fixée à "Non significatif" dans les représentations graphiques.

Mise à jour des données :

Les données proposées en téléchargement dans l’onglet « Export » sont mises à jour chaque année (données de la France entière de 2015 à 2022).

Liste des accidents de travail et des maladies professionnelles des agents de la mairie de Digne-les-Bains. Ce fichier permet de connaître la date, le lieu, la durée, le type d'accident du travail et le cadre d'emplois de l'agent. Le fichier compressé permet d'extraire des données au format XLS, ODS et CSV : DIGNE-AT-MP-2017 DIGNE-AT-MP-2018

PlantDoc is a dataset for visual plant disease detection. The dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images.

Cette base de données contient l'ensemble des remboursements mensuels effectués par le régime général de l'Assurance Maladie (hors prestations hospitalières) par type de prestations (soins et prestations en espèces), type d'exécutant (médecins par spécialité, chirurgiens dentistes, auxiliaires médicaux, laboratoires d'analyse, pharmaciens, ...) et par type de prescripteurs. Les dépenses sont indiquées en montants remboursés et présentées au remboursement. À partir du 1er janvier 2020, le champ des remboursements du régime général de l’Assurance Maladie évolue en intégrant ceux des patients relevant antérieurement du régime social des indépendants (RSI). La variable PRS_FAC_TOP est introduite, afin de permettre la distinction du régime général stricto sensu et de l’ex-régime social des indépendants. Concernant les dépenses de santé d'assurance maladie, deux autres jeux de données, couvrant des champs différents, sont proposés : Dépenses annuelles d'assurance maladie : tableaux de bord portant sur l'ensemble des remboursements (y compris les prestations hospitalières) effectués par le régime général de l'Assurance Maladie en France métropolitaine ; Open Damir : base complète sur les dépenses d'assurance maladie inter régimes : base de données regroupant l'ensemble des remboursements (y compris prestations hospitalières facturées directement à l'Assurance Maladie) effectués par tous les régimes d'assurance maladie en France.

Here are a few use cases for this project:

1. Agricultural Health Monitoring: The "Plant-diseases" model can be used by farmers to monitor the health status of their crops in real time. Identifying diseases early can help apply preventative measures promptly, significantly improving crop yield.

2. Digital Farming Apps: This model can be incorporated into agricultural mobile applications to help farmers identify plant diseases by simply taking a picture of their crops. The app can provide instant diagnosis and treatment recommendations.

3. Research and Development: The "Plant-diseases" model can provide valuable data for agricultural researchers studying crop diseases. By using the model, researchers can quickly categorize different types of plant diseases and identify patterns and correlations.

4. Smart Farming Equipment: This model can be integrated into smart farming equipment such as drones or robotics for crop surveillance. Such equipment can scan large areas of field quickly, identifying unhealthy plants and alerting farmers about potential disease outbreaks.

5. Educational Tool: Institutions teaching agriculture or botany-related subjects can use this model as a practical tool for educating students about plant diseases. They can easily show what different types of diseases look like and explain how they affect plants.

The employees of Caisse nationale de l'assurance maladie with headquarters in France amounted to 69.54 thousand in 2023. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2019 this is a total decrease by approximately 2.7 thousand. The trend from 2019 to 2023 shows, however, that this decrease did not happen continuously.

The total equity of Caisse nationale de l'assurance maladie with headquarters in France amounted to 760.93 million euros in 2023. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2019 this is a total increase by approximately 15 billion euros. The trend from 2019 to 2023 shows ,however, that this increase did not happen continuously.

La taille du marché du marché concurrentiel des médicaments par la maladie de Parkinsons est classé en fonction de produit (agonistes de dopamine, inhibiteurs de MAO-B, lévodopa / carbidopa, anticholinergiques, catechol-o-méthyltransférase (comt) inhibiteurs) et Application (hôpital, clinique, soins à domicile, autres) et régions géographiques (Amérique du Nord, Europe, Asie-Pacifique, Amérique du Sud, Moyen-Orient et Afrique).

Ce rapport donne un aperçu de la taille du marché et prévoit la valeur du marché, exprimée en millions USD, à travers ces segments définis.

Maladies Plantes

## Overview

Maladies Plantes is a dataset for instance segmentation tasks - it contains Cacao Plantains Mais annotations for 687 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The net income of Caisse nationale de l'assurance maladie with headquarters in France amounted to -11.14 billion euros in 2023. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2019 this is a total decrease by approximately 9.67 billion euros. The trend from 2019 to 2023 shows ,however, that this decrease did not happen continuously.

During the COVID-19 pandemic, the national monitoring project "COVID-19 Barometer" was started by the "Consortium Eerstelijnsdata". The aim of this barometer is follow-up of the extra muros (general practitioners, coordinating and advisory physicians, triage posts, pharmacists, midwives, home nurses, ...) with regard to the execution of the critical tasks, the quality of execution of tasks, the need for help, the available capacity, the number infections, and the availability of medical equipment.

This data also aims to support the COVID19 Risk Assessment Group, COVID19 Risk Management Group, and the National Crisis Cell.

This registration was notified to, and assessed and authorized by, the Chamber Social Security and Health of the Information Security Committee (Reference IVC / KSZG / 20/134).

This dataset is about artists and is filtered where the artworks is La Maladie de l'Amour. It has 9 columns such as artist, artworks, birth date, country, and creation start dates. The data is ordered by number of artworks (descending).

The authors of Cocoa Diseases (YOLOv4): Monilia & Phytophthora (Diseases in Cocoa Pods) claim that the total production of Theobroma cacao L cocoa in Colombia for 2018 exceeded 56 thousand tons, making it the second-highest in history despite a 6% reduction compared to 2017. The decrease in production was attributed to factors such as flowering flows, increased incidence of the Monilia disease, and floods caused by heavy rainfall early in the year. In response to the need for reliable inspection procedures to assess crop infections, the authors of the dataset developed a mobile application prototype that utilizes artificial intelligence techniques and image analysis to identify diseased cocoa pods.

Taro Leaf Blight, caused by the pathogen Phytophthora colocasiae, manifests as distinct necrotic spots with white sporangia bands and orange droplets on the leaves. Thriving in temperatures between 25°C to 30°C, the disease spreads rapidly through rain splash and wind-blown spray. This blight not only reduces yields but also affects the income of smallholder farmers who depend heavily on taro cultivation. In recent years, taro yields in Africa have declined due to this disease, combined with other factors such as limited input utilization and cultivation on less fertile lands.

Early detection of Taro Leaf Blight is essential for effective management and prevention. Technologies such as smartphone-based apps, handheld spectrometers, drone-mounted sensors, and biosensors are being explored to enable real-time disease identification. These methods empower farmers to implement timely measures, thus minimizing yield losses and preserving crop quality. However, challenges like the financial barriers of advanced technologies and the need for technical knowledge pose limitations, especially for smallholder farmers in developing countries.

A critical part of combating TLB is building robust datasets for training deep-learning models for disease detection. To this end, a meticulous data collection effort was undertaken by teams in Nigeria and Ghana. This initiative focused on capturing images of taro plants at various stages of TLB infection—Taro Early Blight, Taro Mid Blight, Taro Late Blight, and Taro Healthy. The result of the initiative is the Taro Leaf Blight Disease Image Dataset.

The dataset consists of 18,248 images. The breakdown of each class is as follows: Taro-Late: 1,270 Taro-Mid: 3,370 Taro-Early: 4,864 Taro-Healthy: 8,744 Taro-Not-Early: 4,640 (Combination of Taro-Late & Taro-Mid) Each image is a JPG (RGB) of size 500x500.

Support for implementation of project activities was made possible by the Research Grant (109705-001/002) by the Responsible Artificial Intelligence Network for Climate Action in Africa (RAINCA) consortium made up of WASCAL, RUFORUM and AKADEMIYA2063 provided by IDRC.

This dataset is curated for the purpose of detecting and classifying four distinct types of rice leaf diseases: Hispa, Brown Spot, Leaf Blast, Leaf Scaled,Narraow brown spot and Neck Blast. Each category represents a specific pathology affecting rice plants. The dataset provides annotated images to facilitate the training and evaluation of machine learning models aimed at automating the detection of these diseases. Researchers and enthusiasts in the field of agricultural technology and plant pathology can leverage this dataset to develop robust solutions for early diagnosis and effective management of rice crop health.

No description was included in this Dataset collected from the OSF

Objectifs de croissance des dépenses de santé Export : CSV, HTML et XLS Source : www.ecosante.fr Irdes d'après données Comptes de la Sécurité Sociale, Ondam