According to a ranking by Statista and Newsweek, the world's best hospital is the *********** in Rochester, Minnesota. A total of **** U.S. hospitals made it to the top ten list, while one hospital in each of the following countries was also ranked among the top ten best hospitals in the world: Canada, Sweden, Germany, Israel, Singapore, and Switzerland.

Means, standard deviations (SD) of hospital costs (in CHF), and results of independent t-tests across different hospital types.

Abstract

Collecting patients’ experiences with care provision is essential to evaluate the quality of care in general, and responsiveness of care in particular, one of the core dimensions of high-quality care. After a first study conducted in 2018, we conducted a second study to collect patient experience data. Our main study objective was to explore experiences of care of people treated for any type of cancer in eight hospitals in the French- and German-speaking regions of Switzerland, and to explore whether these experiences differed by linguistic region, hospital, and cancer type.

    The Swiss Cancer Patient Experience-2 (SCAPE) was a cross-sectional multicenter survey, conducted between September 2021 and February 2022, among cancer patients diagnosed with any type of cancer from four hospitals in the French-speaking region and from four hospitals in the German-speaking region. Data were collected with a self-administered questionnaire, including questions on experiences of care and the impact of COVID-19 on cancer care and patients as well as socio-demographic and clinical characteristics. Of the 6873 adult patients invited to complete the questionnaire, 3220 patients returned it (47% response rate) and were included in the analyses. 

    Patients rated their overall care at 8.9 on average on a 0-10 scale. Overall, experiences of care with diagnostic tests were positive, particularly the waiting time between the prescription of an examination and its completion, the usefulness of the tests performed, the trust in hospital staff and the fact that care was provided with respect and dignity. The experience is less positive with respect to information received at diagnosis, support for short- and long-term side effects of treatment and cancer, information about the impact of cancer on daily activities, difficulty finding a staff member to talk about concerns and fears, financial aspects of the disease, and loved ones’ involvement.

Geographic coverage

French- and German-speaking regions of Switzerland

Analysis unit

Individual. N=3220

Universe

Adult patients diagnosed with any type of cancer recruited from four hospitals in the French-speaking region – Lausanne University Hospital (CHUV), Hôpital Fribourgeois (HFR), Geneva University Hospitals (HUG), Hôpital du Valais (HVS) and from four hospitals in the German-speaking region - Cantonal Hospital of Grisons (KSGR), Luzern Cantonal Hospital (LUKS), University Hospital Zurich (USZ), Zug Cantonal Hospital (ZGKS).

Kind of data

Self-reported data collected from paper and online questionnaire

Sampling procedure

All patients meeting inclusion criteria.

Mode of data collection

Paper and online questionnaire, self-administered at home.

Research instrument

SCAPE-2 Questionnaire including 128 closed questions (79 questions on experiences of care; 23 questions on the impact of COVID-19; 12 questions on health status; 14 questions on socio-demographic characteristics) and 3 free-text sections.

Cleaning operations

Data from paper and online questionnaires were merged after careful verification of all coding. Data were checked for inconsistency (multiple check marks when only one allowed). We also considered hand written comments next to questions to edit the answer if necessary.

A model to predict the mass flows and concentrations of pharmaceuticals predominantly used in hospitals across a large number of sewage treatment plant (STP) effluents and river waters was developed at high spatial resolution. It comprised 427 geo-referenced hospitals and 742 STPs serving 98% of the general population in Switzerland. In the modeled base scenario, domestic, pharmaceutical use was geographically distributed according to the population size served by the respective STPs. Distinct hospital scenarios were set up to evaluate how the predicted results were modified when pharmaceutical use in hospitals was allocated differently; for example, in proportion to number of beds or number of treatments in hospitals. The hospital scenarios predicted the mass flows and concentrations up to 3.9 times greater than in the domestic scenario for iodinated X-ray contrast media (ICM) used in computed tomography (CT), and up to 6.7 times greater for gadolinium, a contrast medium used in magnetic resonance imaging (MRI). Field measurements showed that ICM and gadolinium were predicted best by the scenarios using number of beds or treatments in hospitals with the specific facilities (i.e., CT and/or MRI). Pharmaceuticals used both in hospitals and by the general population (e.g., cyclophosphamide, sulfamethoxazole, carbamazepine, diclofenac) were predicted best by the scenario using the number of beds in all hospitals, but the deviation from the domestic scenario values was only small. Our study demonstrated that the bed number-based hospital scenarios were effective in predicting the geographical distribution of a diverse range of pharmaceuticals in STP effluents and rivers, while the domestic scenario was similarly effective on the scale of large river-catchments.

This dataset contains clinical information collected from patients across multiple hospitals, used to analyze and predict the presence of heart disease.

📊 Source The data has been compiled from publicly available datasets, including contributions from:

Cleveland Clinic Foundation (USA)

Hungarian Institute of Cardiology (Hungary)

University Hospital Zurich (Switzerland)

Long Beach VA Medical Center (USA)

These datasets were originally published as part of the UCI Machine Learning Repository.

🧪 Features (Columns) Typical features in the dataset may include:

age – Age of the patient

sex – Gender (1 = male, 0 = female)

cp – Chest pain type

trestbps – Resting blood pressure

chol – Serum cholesterol in mg/dl

fbs – Fasting blood sugar > 120 mg/dl

restecg – Resting electrocardiographic results

thalach – Maximum heart rate achieved

exang – Exercise induced angina

oldpeak – ST depression (rounded, positive values)

slope – Slope of the peak exercise ST segment

ca – Number of major vessels colored by fluoroscopy

thal – Thalassemia test result

target – Presence of heart disease (1 = disease, 0 = no disease)

🏥 Source Tag An additional column called "source" may be included to identify the source hospital for each record.

The U.S., followed by Switzerland, had the highest average cost per day to stay in a hospital as of 2015. At that time the hospital costs per day in the U.S. were on average 5,220 U.S. dollars. In comparison, the hospital costs per day in Spain stood at an average of 424 U.S. dollars. Even Switzerland, also a very expensive country, had significantly lower costs than the United States.

Number of U.S. hospitals

The number of U.S. hospitals has decreased in recent years with some increase in 2017. There are several types of hospitals in the U.S. with different ownerships. In general there are more hospitals with a non-profit ownership in the U.S. than there are hospitals with state/local government or for-profit ownership.

U.S. hospital costs

Health care expenditures in the U.S. are among the highest in the world. By the end of 2019, hospital care expenditures alone across the U.S. are expected to exceed 1.2 trillion U.S. dollars. Among the most expensive medical conditions treated in U.S. hospitals are septicemia, osteoarthritis and live births. There are different ways to pay for hospital costs in the United States. Among all payers of U.S. hospital costs, Medicare and private payers are paying the largest proportion of all costs.

Means, standard deviations (SD) and ranges of hospital costs (in CHF).

Yearly citation counts for the publication titled "Is laparoscopic major hepatectomy feasible and safe in Swiss cantonal hospitals?".

Objectives: To determine the proportion of patients who received a treatment for Clostridioides difficile infection (CDI) among those presenting a discordant Clostridioides difficile diagnostic assay and to identify patient characteristics associated with the decision to treat CDI.

Design: Cross-sectional study.

Setting: Monocentric study in a tertiary care hospital, Geneva, Switzerland

Participants: Among 4562 adult patients tested for C. difficile between March 2017 and March 2019, 208 patients with discordant tests’ results (positive nucleic acid amplification test [NAAT+]/negative enzyme immunoassay [EIA-]) were included.

Main outcome measures: Treatment for CDI.

Results: CDI treatment was administered in 147 (71%) cases. In multivariate analysis, an abdominal computed tomography scan with signs of colitis (OR 14.7; 95% CI 1.96-110.8) was the only factor associated with CDI treatment.

Conclusions: The proportion of NAAT+/EIA- patients who received treatment questions the contribution of the EIA for the detection of toxin A/B after NAAT to limit overtreatment. Additional studies are needed to investigate if other factors are associated with the decision to treat.

Methods We conducted a cross-sectional study at Geneva University Hospitals, a 2000-bed Swiss tertiary care centre. Clinical and biological data (results of NAAT/EIA assays performed on stool samples) were collected from electronic medical records (EMR) and the hospital bacteriology laboratory, respectively. Inclusion criteria were all adult patients (≥18 years) hospitalised or not, with C. difficile toxin assays performed on stool samples between 1 March 2017 and 1 March 2019 that yielded discordant results (NAAT+/EIA-). Exclusion criteria were asymptomatic patients (without diarrhoea, ileus or toxic megacolon), paediatric patients, patients with a treatment against C. difficile introduced ≥ 48 h before the results of tests, or without clinical data available in EMR form. In patients presenting several tests with discordant results over the study period, only the first test was considered for analysis. The study was approved by the Geneva cantonal ethics commission and a waiver of informed consent was granted due to its retrospective nature

Pearson correlations (incl. sample sizes (n), means, and standard deviations (SD)) of predictor variables with hospital costs.

BackgroundMulti-substance use is accompanied by increased morbidity and mortality and responsible for a large number of emergency department (ED) consultations. To improve the treatment for this vulnerable group of patients, it is important to quantify and break down in detail the ED resources used during the ED treatment of multi-substance users.MethodsThis retrospective single centre case-control study included all ED consultations of multi-substance users over a three-year study period at a university hospital in Switzerland. Resource consumption of these patients was compared to an age-matched control group of non-multi-substance users.ResultsThe analysis includes 867 ED consultations of multi-substance users compared to 4,335 age-matched controls (5:1). Multi-substance users needed more total resources (median tax points medical currency: 762 (459–1226) vs. 462 (196–833), p<0.001), especially physician, radiology, and laboratory resources. This difference persisted in multivariable analysis (geometric mean ratio (GMR) 1.2, 95% CI: 1.1–1.3, p = 0.001) adjusted for sociodemographic parameters, consultation characteristics, and patient comorbidity; the GMR was highest in ED laboratory and radiology resource consumption. Among multi-substance user, indirect and non-drug-related consultations had higher ED resource consumption compared to drug-related consultations. Furthermore, leading discipline as well as urgency were predictors of ED resource consumption. Moreover, multi-substance users had more revisits (55.2% vs. 24.9%, p<0.001) as well as longer ED and in-hospital stays (both: GMR 1.2, 95% CI: 1.1–1.3, p<0.001).ConclusionED consultations of multi-substance users are expensive and resource intensive. Multi-substance users visited the ED more often and stayed longer at the ED and in-hospital. The findings of our study underline the importance of this patient group. Additional efforts should be made to improve their ED care. Special interventions should target this patient group in order to decrease the high frequency and costs of emergency consultations caused by multi-substance users.

Descriptive statistics (means, standard deviations (SD), and ranges) of selected variables.

The Epinettes database is the collection of clinical and neuropsychological data, acquired during routine care of patients consulting at the Memory Centre of the University Hospital of Geneva in Switzerland. The database comprises different diagnosis groups, hence allows observational study. It is a transversal data set, as it aims to compare the groups, as well as a longitudinal, as it aims to analyse the progression of each type of dementia.

Context

This is a multivariate type of dataset which means providing or involving a variety of separate mathematical or statistical variables, multivariate numerical data analysis. It is composed of 14 attributes which are age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise-induced angina, oldpeak — ST depression induced by exercise relative to rest, the slope of the peak exercise ST segment, number of major vessels and Thalassemia. This database includes 76 attributes, but all published studies relate to the use of a subset of 14 of them. The Cleveland database is the only one used by ML researchers to date. One of the major tasks on this dataset is to predict based on the given attributes of a patient that whether that particular person has heart disease or not and other is the experimental task to diagnose and find out various insights from this dataset which could help in understanding the problem more.

Content

Column Descriptions:

1. id (Unique id for each patient)
2. age (Age of the patient in years)
3. origin (place of study)
4. sex (Male/Female)
5. cp chest pain type ([typical angina, atypical angina, non-anginal, asymptomatic])
6. trestbps resting blood pressure (resting blood pressure (in mm Hg on admission to the hospital))
7. chol (serum cholesterol in mg/dl)
8. fbs (if fasting blood sugar > 120 mg/dl)
9. restecg (resting electrocardiographic results) -- Values: [normal, stt abnormality, lv hypertrophy]
10. thalach: maximum heart rate achieved
11. exang: exercise-induced angina (True/ False)
12. oldpeak: ST depression induced by exercise relative to rest
13. slope: the slope of the peak exercise ST segment
14. ca: number of major vessels (0-3) colored by fluoroscopy
15. thal: [normal; fixed defect; reversible defect]
16. num: the predicted attribute

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.

Relevant Papers:

• Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64,304--310. Web Link
  
• David W. Aha & Dennis Kibler. "Instance-based prediction of heart-disease presence with the Cleveland database." Web Link
  
• Gennari, J.H., Langley, P, & Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11--61. Web Link
  

Citation Request:

The authors of the databases have requested that any publications resulting from the use of the data include the names of the principal investigator responsible for the data collection at each institution. They would be:

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.

The number of hospitals in Austria has remained relatively stable since 2000. In 2022, there were 262 hospitals in the country. Healthcare workers in Austria Although the number of healthcare institutions has remained relatively stable year-on-year in Austria, the number of personnel working within them has been increasing. The number of physicians in Austria increased by approximately 9.3 thousand between 2010 and 2022. In addition, the number of practicing nurses came to almost 95 thousand in 2021, an increase of roughly 48 thousand nurses since 2000. Spending indicators on health In every year since 2009, Austria has spent over ten percent of its GDP on healthcare. In 2023, Austria’s expenditure on healthcare was 11 percent of GDP. In comparison to other European countries this placed Austria fourth-highest in terms of health expenditure in 2023. At the top of the list was Switzerland, which spent 12 percent of GDP on healthcare in this year.

Context

The data is already presented in https://www.kaggle.com/ronitf/heart-disease-uci but there are some descriptions and values that are wrong as discussed in https://www.kaggle.com/ronitf/heart-disease-uci/discussion/105877. So, here is re-processed dataset that was cross-checked with the original data https://archive.ics.uci.edu/ml/datasets/Heart+Disease.

Content

There are 13 attributes 1. age: age in years 2. sex: sex (1 = male; 0 = female) 3. cp: chest pain type -- Value 0: typical angina -- Value 1: atypical angina -- Value 2: non-anginal pain -- Value 3: asymptomatic 4. trestbps: resting blood pressure (in mm Hg on admission to the hospital) 5. chol: serum cholestoral in mg/dl 6. fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) 7. restecg: resting electrocardiographic results -- Value 0: normal -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) -- Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria 8. thalach: maximum heart rate achieved 9. exang: exercise induced angina (1 = yes; 0 = no) 10. oldpeak = ST depression induced by exercise relative to rest 11. slope: the slope of the peak exercise ST segment -- Value 0: upsloping -- Value 1: flat -- Value 2: downsloping 12. ca: number of major vessels (0-3) colored by flourosopy 13. thal: 0 = normal; 1 = fixed defect; 2 = reversable defect and the label 14. condition: 0 = no disease, 1 = disease

Acknowledgements

Data posted on Kaggle: https://www.kaggle.com/ronitf/heart-disease-uci Description of the data above: https://www.kaggle.com/ronitf/heart-disease-uci/discussion/105877 Original data https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbr Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. 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

Inspiration

With the attributes described above, can you predict if a patient has heart disease?

This file describes the contents of the heart-disease directory.

This directory contains 4 databases concerning heart disease diagnosis. All attributes are numeric-valued. The data was collected from the four following locations:

 1. Cleveland Clinic Foundation (cleveland.data)
 2. Hungarian Institute of Cardiology, Budapest (hungarian.data)
 3. V.A. Medical Center, Long Beach, CA (long-beach-va.data)
 4. University Hospital, Zurich, Switzerland (switzerland.data)

Each database has the same instance format. While the databases have 76 raw attributes, only 14 of them are actually used. Thus I've taken the liberty of making 2 copies of each database: one with all the attributes and 1 with the 14 attributes actually used in past experiments.

The authors of the databases have requested:

 ...that any publications resulting from the use of the data include the 
 names of the principal investigator responsible for the data collection
 at each institution. They would be:

  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.

Thanks in advance for abiding by this request.

David Aha July 22, 1988

1. Title: Heart Disease Databases

2. Source Information: (a) 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. (b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779
   (c) Date: July, 1988

3. Past Usage:

   1. Detrano,~R., Janosi,~A., Steinbrunn,~W., Pfisterer,~M., Schmid,~J., Sandhu,~S., Guppy,~K., Lee,~S., & Froelicher,~V. (1989). {\it International application of a new probability algorithm for the diagnosis of coronary artery disease.} {\it American Journal of Cardiology}, {\it 64},304--310. -- International Probability Analysis -- Address: Robert Detrano, M.D. Cardiology 111-C V.A. Medical Center 5901 E. 7th Street Long Beach, CA 90028 -- Results in percent accuracy: (for 0.5 probability threshold) Data Name: CDF CADENZA -- Hungarian 77 74 Long beach 79 77 Swiss 81 81 -- Approximately a 77% correct classification accuracy with a logistic-regression-derived discriminant function

   2. David W. Aha & Dennis Kibler

      -- Instance-based prediction of heart-disease presence with the Cleveland database -- NTgrowth: 77.0% accuracy -- C4: 74.8% accuracy

   3. John Gennari -- Gennari, J.~H., Langley, P, & Fisher, D. (1989). Models of incremental concept formation. {\it Artificial Intelligence, 40}, 11--61. -- Results: -- The CLASSIT conceptual clustering system achieved a 78.9% accuracy on the Cleveland database.

4. Relevant Information: This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "goal" field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).

   The names and social security numbers of the patients were recently removed from the database, replaced with dummy values.

   One file has been "processed", that one containing the Cleveland database. All four unprocessed files also exist in this directory.

5. Number of Instances: Database: # of instances: Cleveland: 303 Hungarian: 294 Switzerland: 123 Long Beach VA: 200

6. Number of Attributes: 76 (including the predicted attribute)

7. Attribute Information: -- Only 14 used -- 1. #3 (age)
   -- 2. #4 (sex)
   -- 3. #9 (cp)
   -- 4. #10 (trestbps)
   -- 5. #12 (chol)
   -- 6. #16 (fbs)
   -- 7. #19 (restecg)
   -- 8. #32 (thalach)
   -- 9. #38 (exang)
   -- 10. #40 (oldpeak)
   -- 11. #41 (slope)
   -- 12. #44 (ca)
   -- 13. #51 (thal)
   -- 14. #58 (num) (the predicted attribute)

   -- Complete attribute documentation: 1 id: patient identification number 2 ccf: social security number (I replaced this with a dummy value of 0) 3 age: age in years 4 sex: sex (1 = male; 0 = female) 5 painloc: chest pain location (1 = substernal; 0 = otherwise) 6 painexer (1 = provoked by exertion; 0 = otherwise) 7 relrest (1 = relieved after rest; 0 = otherwise) 8 pncaden (sum of 5, 6, and 7) 9 cp: chest pain type -- Value 1: typical angina -- Value 2: atypical angina -- Value 3: non-anginal pain -- Value 4: asymptomatic 10 trestbps: resting blood pressure (in mm Hg on admission to the hospital) 11 htn 12 chol: serum cholestoral in mg/dl 13 smoke: I believe this is 1 = yes; 0 = no (is or is not a smoker) 14 cigs (cigarettes per day) 15 years (number of years as a smoker) 16 fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) 17 dm (1 = history of diabetes; 0 = no such history) 18 famhist: family history of coronary artery disease (1 = yes; 0 = no) 19 restecg: resting electrocardiographic results -- Value 0: normal -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) -- Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria 20 ekgmo (month of exercise ECG reading) 21 ekgday(day of exercise ECG reading) 22 ekgyr (year of exercise ECG reading) 23 dig (digitalis used furing exercise ECG: 1 = yes; 0 = no) 24 prop (Beta blocker used during exercise ECG: 1 = yes; 0 = no) 25 nitr (nitrates used during exercise ECG: 1 = yes; 0 = no) 26 pro (calcium channel blocker used during exercise ECG: 1 = yes; 0 = no) 27 diuretic (diuretic used used during exercise ECG: 1 = yes; 0 = no) 28 proto: exercise protocol 1 = Bruce
   2 = Kottus 3 = McHenry 4 = fast Balke 5 = Balke 6 = Noughton 7 = bike 150 kpa min/min (Not sure if "kpa min/min" is what was written!) 8 = bike 125 kpa min/min
   9 = bike 100 kpa min/min 10 = bike 75 kpa min/min 11 = bike 50 kpa min/min 12 = arm ergometer 29 thaldur: duration of exercise test in minutes 30 thaltime: time when ST measure depression was noted 31 met: mets achieved 32 thalach: maximum heart rate achieved 33 thalrest: resting heart rate 34 tpeakbps: peak exercise blood pressure (first of 2 parts) 35 tpeakbpd: peak exercise blood pressure (second of 2 parts) 36 dummy 37 trestbpd: resting blood pressure 38 exang: exercise induced angina (1 = yes; 0 = no) 39 xhypo: (1 = yes; 0 = no) 40 oldpeak = ST depression induced by exercise relative to rest 41 slope: the slope of the peak exercise ST segment -- Value 1: upsloping -- Value 2: flat -- Value 3: downsloping 42 rldv5: height at rest 43 rldv5e: height at peak exercise 44 ca: number of major vessels (0-3) colored by flourosopy 45 restckm: irrelevant 46 exerckm: irrelevant 47 restef: rest raidonuclid (sp?) ejection fraction 48 restwm: rest wall (sp?) motion abnormality 0 = none 1 = mild or moderate 2 = moderate or severe 3 = akinesis or dyskmem (sp?) 49 exeref: exercise radinalid (sp?) ejection fraction 50 exerwm: exercise wall (sp?) motion 51 thal: 3 = normal; 6 = fixed defect; 7 = reversable defect 52 thalsev: not used 53 thalpul: not used 54 earlobe: not used 55 cmo: month of cardiac cath (sp?) (perhaps "call") 56 cday: day of cardiac cath (sp?) 57 cyr: year of cardiac cath (sp?) 58 num: diagnosis of heart disease (angiographic disease status) -- Value 0: < 50% diameter narrowing -- Value 1: > 50% diameter narrowing (in any major vessel: attributes 59 through 68 are vessels) 59 lmt 60 ladprox 61 laddist 62 diag 63 cxmain 64 ramus 65 om1 66 om2 67 rcaprox 68 rcadist 69 lvx1: not used 70 lvx2: not used 71 lvx3: not used 72 lvx4: not used 73 lvf: not used 74 cathef: not used 75 junk: not used 76 name: last name of patient (I replaced this with the dummy string "name")

8. Missing Attribute Values: Several. Distinguished with value -9.0.

9. Class Distribution: Database: 0 1 2 3 4 Total Cleveland: 164 55 36 35 13 303 Hungarian: 188 37 26 28 15 294 Switzerland: 8 48 32 30 5 123 Long Beach VA: 51 56 41 42 10 200

Context This is a multivariate type of dataset which means providing or involving a variety of separate mathematical or statistical variables, multivariate numerical data analysis. It is composed of 14 attributes which are age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise-induced angina, oldpeak — ST depression induced by exercise relative to rest, the slope of the peak exercise ST segment, number of major vessels and Thalassemia. This database includes 76 attributes, but all published studies relate to the use of a subset of 14 of them. The Cleveland database is the only one used by ML researchers to date. One of the major tasks on this dataset is to predict based on the given attributes of a patient that whether that particular person has heart disease or not and other is the experimental task to diagnose and find out various insights from this dataset which could help in understanding the problem more.

Content

Column Descriptions:

id (Unique id for each patient)

age (Age of the patient in years)

origin (place of study)

sex (Male/Female)

cp chest pain type ([typical angina, atypical angina, non-anginal, asymptomatic])

trestbps resting blood pressure (resting blood pressure (in mm Hg on admission to the hospital))

chol (serum cholesterol in mg/dl)

fbs (if fasting blood sugar > 120 mg/dl)

restecg (resting electrocardiographic results)

-- Values: [normal, stt abnormality, lv hypertrophy]

thalach: maximum heart rate achieved

exang: exercise-induced angina (True/ False)

oldpeak: ST depression induced by exercise relative to rest

slope: the slope of the peak exercise ST segment

ca: number of major vessels (0-3) colored by fluoroscopy

thal: [normal; fixed defect; reversible defect]

num: the predicted attribute

Acknowledgements

Creators:

Cleveland Clinic Foundation: The dataset was collected from patients at the Cleveland Clinic. It was used in various studies to understand the factors contributing to heart disease. UCI Machine Learning Repository: The dataset is hosted on this platform, which is a well-known resource for machine learning datasets. The repository provides a wide range of datasets for educational and research purposes.

Relevant Papers:

Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64,304--310. Web Link David W. Aha & Dennis Kibler. "Instance-based prediction of heart-disease presence with the Cleveland database." Web Link Gennari, J.H., Langley, P, & Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11--61. Web Link Citation Request: The authors of the databases have requested that any publications resulting from the use of the data include the names of the principal investigator responsible for the data collection at each institution. They would be:

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

Distribution (N records, %) of variables related to health status and hospital stay with descriptive statistics (mean (SD), median (IQR)) of length of stay and number of side diagnoses and percentage (%) of transfer to inpatient setting = yes.

In 2024, the United States had the ******* per capita health expenditure among OECD countries. At that time, per capita health expenditure in the U.S. amounted over ******** U.S. dollars, significantly higher than in Switzerland, the country with the ************** per capita health expenditure. Norway, Germany and, the Netherlands are also within the top five countries with the highest per capita health expenditure. The United States also spent the highest share of it’s gross domestic product on health care, with **** percent of its GDP spent on health care services. Health Expenditure in the U.S. The United States is the highest spending country worldwide when it comes to health care. In 2023, total health expenditure in the U.S. came close to **** trillion dollars. Expenditure as a percentage of GDP is projected to increase to approximately ** percent by the year 2033. Distribution of Health Expenditure in the U.S. Health expenditure in the United States is spread out across multiple categories such as nursing home facilities, home health care, and prescription drugs. As of 2023, the majority of health expenditure in the United States was spent on hospital care, accounting for a bit less than *** third of all health spending. Hospital care was followed by spending on physician and clinical services which accounted for ** percent of overall health expenditure.