Eye Disease Dataset

  Description

The Eye Disease Dataset is a collection of images related to various eye diseases. It provides a valuable resource for training and evaluating computer vision models for eye disease detection and classification. The dataset includes images representing five different eye disease classes: Bulging Eyes, Cataracts, Crossed Eyes, Glaucoma, and Uveitis.

  Dataset Details

Dataset Name: Falah/eye-disease-dataset

Number of… See the full description on the dataset page: https://huggingface.co/datasets/Falah/eye-disease-dataset.

Context

The dataset is the Cleveland Heart Disease dataset taken from the UCI repository. The dataset consists of 303 individuals’ data. There are 14 columns in the dataset(which have been extracted from a larger set of 75). No missing values. The classification task is to predict whether an individual is suffering from heart disease or not. (0: absence, 1: presence)

original data: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Content

This database contains 13 attributes and a target variable. It has 8 nominal values and 5 numeric values. The detailed description of all these features are as follows:

1. Age: Patients Age in years (Numeric)
2. Sex: Gender (Male : 1; Female : 0) (Nominal)
3. cp: Type of chest pain experienced by patient. This term categorized into 4 category. 0 typical angina, 1 atypical angina, 2 non- anginal pain, 3 asymptomatic (Nominal)
4. trestbps: patient's level of blood pressure at resting mode in mm/HG (Numerical)
5. chol: Serum cholesterol in mg/dl (Numeric)
6. fbs: Blood sugar levels on fasting > 120 mg/dl represents as 1 in case of true and 0 as false (Nominal)
7. restecg: Result of electrocardiogram while at rest are represented in 3 distinct values 0 : Normal 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) 2: showing probable or definite left ventricular hypertrophyby Estes' criteria (Nominal)
8. thalach: Maximum heart rate achieved (Numeric)
9. exang: Angina induced by exercise 0 depicting NO 1 depicting Yes (Nominal)
10. oldpeak: Exercise induced ST-depression in relative with the state of rest (Numeric)
11. slope: ST segment measured in terms of slope during peak exercise
    0: up sloping; 1: flat; 2: down sloping(Nominal)
12. ca: The number of major vessels (0–3)(nominal)
13. thal: A blood disorder called thalassemia 0: NULL 1: normal blood flow 2: fixed defect (no blood flow in some part of the heart) 3: reversible defect (a blood flow is observed but it is not normal(nominal)
14. target: It is the target variable which we have to predict 1 means patient is suffering from heart disease and 0 means patient is normal.

Variable to be predicted

Absence (1) or presence (2) of heart disease

Cost Matrix

 abse pres

absence 0 1 presence 50

where the rows represent the true values and the columns the predicted.

No missing values.

303 observations

Acknowledgements

Creators: 1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. 2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. 3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. 4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

similar dataset : https://www.kaggle.com/datasets/ritwikb3/heart-disease-statlog

Predicting Coronary Heart Disease by Non-Invasive Means

Le système d’assurance maladie créé en 1945 permet à la France d’afficher de bons résultats en termes d’espérance de vie. Pour autant, la prévalence de pratiques à risque, un taux de mortalité infantile élevé et des inégalités croissantes d’accès aux soins nuancent ces résultats, obtenus en outre au prix de déficits récurrents. La France se caractérise aussi par une dépense de santé élevée en proportion du PIB et par la part importante des assurances complémentaires dans son financement. Face à l’augmentation structurelle des dépenses, alors que les outils actuels de régulation ont atteint leurs limites, la qualité et l’égalité d’accès aux soins ne pourront être maintenues ou renforcées qu’en réformant l’organisation et la gestion du système de santé. Ce rapport est accessible sur le site de la Cour. Les fichiers publiés correspondent aux données ayant servi de base à l'élaboration du rapport.

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).

Daily situation updates and data regarding the COVID-19 outbreak

• Figure 1: Countries, territories or areas with reported confirmed cases of COVID-19.
• Table 1: Confirmed and suspected cases of COVID-19 acute respiratory disease reported by provinces, regions and cities in China.
• Table 2: Countries, territories or areas outside China with reported laboratory-confirmed COVID-19 cases and deaths.
• Figure 2: Epidemic curve of confirmed COVID-19 cases reported outside of China, by date of report and WHO region.

The dataset contains tweets in French mentioning natural hazards (pests and diseases), between 2008 and 2021.

pest_tweets_without_userid.csv, disease_tweets_without_userid.csv, JNO_tweets.csv and pyrale_tweets.csv, corvidae_tweets.csv tweet_RT are collections of tweets, each of these .csv files contains all or several of the following columns:

• time: the timestamp of the tweet
• twt_id: the ID of the tweet
• text: text content of the tweet
• tag: the query string that we used when we called twitter search API to harvest the tweet
• author_id: ID of the author, corresponding to user_id in tweet_user.csv
• referenced_tweets: empty, a list of objects of referenced tweets and the reference type(reply, retweet,...)
• crop_tags: empty, or potential crop name
• geo: empty, or ID of the place, corresponding to place_id in tweet_places.csv

tweet_users.csv is a list of the users of the collected tweets. It contains the following columns:

• user_id: the ID of the user
• name: the name of the user, as they’ve defined it on their profile
• username: the Twitter screen name, handle, or alias that this user identifies themselves with
• description: empty, or user's public profile

tweet_places.csv is a table of all mentioned places in the tweet collections. It contains the following columns:

• place_id: the ID of the place, if this is a point of interest tagged in the Tweet.
• name: the short name of this place.
• country: the full-length name of the country this place belongs to.
• place_type: Specified the particular type of information represented by this place information, such as a city name, or a point of interest.
• geo: Contains place details in GeoJSON format.

Data are downloaded from PADI-web for the period from 1st January to 28th June 2016 for the four studied diseases, i‧e. African swine fever (ASF), foot-and-mouth disease (FMD), bluetongue (BTV), and avian influenza (AI). This dataset indicates information associated with "extraction information" task with PADI-web (e‧g. countries, dates, symptoms, etc.) for each disease (e‧g. ASF, FMD, BTV, AI). Article_user_label indicates the manually evaluations of articles. Note that it was easy to evaluate the articles/signals that were relevant (outbreak-related) or irrelevant (not relevant to the article nor to the extracted info) or even potentially relevant articles (economy, vaccination, etc). For some diseases (like influenza) there are other categories: - Potentially/Irrelevant where the article was relevant or potentially relevant, but the extraction was irrelevant - Irrelevant/Potentially where the article was irrelevant, but the information extraction was relevant Les données sont téléchargées sur PADI-web pour la période du 1er janvier au 28 juin 2016 pour les quatre maladies étudiées, à savoir la peste porcine africaine (PPA), la fièvre aphteuse (Fièvre aphteuse), la fièvre catarrhale du mouton (Fièvre aphteuse) et la grippe aviaire (GA). Cet ensemble de données indique les informations associées à la tâche "extraction d'informations" avec PADI-web (par ex. pays, dates, symptômes, etc.) pour chaque maladie (par ex. peste porcine africaine, fièvre aphteuse, fièvre aphteuse, TVB, grippe aviaire). Article_user_label indique les évaluations manuelles des articles. Notez qu'il était facile d'évaluer les articles/signaux qui étaient pertinents (liés au foyer) ou non pertinents (sans rapport avec l'article ni avec l'information extraite) ou même les articles potentiellement pertinents (économie, vaccination, etc.). Pour certaines maladies (comme la grippe), il existe d'autres catégories : - Potentiellement ou potentiellement pertinent lorsque l'article était pertinent ou potentiellement pertinent, mais l'extraction n'était pas pertinente - Non pertinent ou potentiellement pertinent lorsque l'article n'était pas pertinent, mais l'extraction d'information était pertinente.

The Plant RNA-Image Repository is a compiled database of plant images and omics data. The dataset contains images of four distinct plant maladies, including powdery mildew, rust, leaf spot, and blight, as well as gene expression and metabolite data. Using a high-resolution camera in a controlled environment at the facility of the various Agriculture Universities of the Khyber Pakhtunkhwa, Pakistan. We captured 26940 images of plants, where each class has different number of samples for each disease type. Each image was labelled with the disease type corresponding to it. The images were preprocessed by resizing them to 224x224 pixels and standardizing the pixel values. In addition to collecting images of the same plants, we also collected gene expression and metabolite data. We extracted RNA from the plant leaves using a commercial reagent and sequenced it on an Illumina HiSeq 4000 platform. The average length of the 100 million pairedend readings obtained was 150 base pairs. The unprocessed reads were trimmed with Trimmomatic and aligned with STAR against the reference genome. We counted the number of reads that mapped to each gene using featureCounts, and then identified differentially expressed genes between healthy and diseased plants using the DESeq2 package in R. Using gas chromatography-mass spectrometry (GC-MS), we gathered additional metabolite information. Using a methanol-water extraction protocol, we extracted metabolites from the plant leaves and analyzed the extracts using GC-MS.

This dataset can be used to classify the following diseases: 1. Actinic keratosis 2. Atopic Dermatitis 3. Benign keratosis 4. Dermatofibroma 5. Melanocytic nevus 6. Melanoma 7. Squamous cell carcinoma 8. Tinea Ringworm Candidiasis 9. Vascular lesion

This is the historic impact factors of Journal Des Maladies Vasculaires computed for each year in CSV format. The first column shows the exaly JournalID for mixing this table with those of other journals

This is the historic impact factors of Medecine Des Maladies Metaboliques computed for each year in CSV format. The first column shows the exaly JournalID for mixing this table with those of other journals

Observation maladies cryptogamiques sur des Témoins Non Traités du réseau BSV

This is the historic impact factors of EMC - Pédiatrie - Maladies Infectieuses computed for each year in CSV format. The first column shows the exaly JournalID for mixing this table with those of other journals

Le présent fichier d'occurrence donne la distribution des vecteurs de maladie au Bénin. A partir des maladies, les vecteurs ont été identifiés à l'aide des spécialistes et des gestionnaires des bases de données.