In 2022, South Korea had the lowest rate of death from ischemic heart disease among OECD countries, with around ** deaths per 100,000 inhabitants. In comparison, there were around *** deaths due to ischemic heart disease per 100,000 population in Lithuania. Cardiovascular disease worldwide Fatty deposits accumulating in the inner wall of the coronary artery that restrict blood flow to the heart cause ischemic heart disease (IHD) and can also precipitate heart attacks and strokes. Cardiovascular risk factors such as smoking, heavy alcohol use, and an unhealthy diet are more prevalent in Eastern European countries, contributing to a much higher burden of cardiovascular diseases and deaths. Prevention and intervention Invasive interventions for heart disease can include surgical procedures such as heart bypass surgery- where blood is diverted around clogged parts of major arteries. Other medical interventions include the use of prescribed or over-the-counter drugs, such as prescription nitrates or beta blockers, or OTC medications like aspirin. Lifestyle factors to lower blood pressure and cholesterol levels can help decrease the risk of heart attacks and other cardiovascular diseases, including maintaining a healthy diet, regular physical activity, and smoking and alcohol cessation.

In 2021, it was estimated that the Pacific island country Nauru had the highest death rate from ischemic heart disease in the world, with around 405 deaths per 100,000 population. In 2021, ischemic heart disease was the leading cause of death worldwide, resulting in over nine million deaths.

Statistical health data visualization showing measure, unit, Alcoholic cardiomyopathy, Aortic aneurysm, Atrial fibrillation and flutter, Cardiomyopathy and myocarditis, Endocarditis, Hypertensive heart disease, Intracerebral hemorrhage, Ischemic heart disease, Ischemic stroke, Lower extremity peripheral arterial disease, Myocarditis, Non-rheumatic calcific aortic valve disease, Non-rheumatic degenerative mitral valve disease, Non-rheumatic valvular heart disease, Other cardiomyopathy, Other cardiovascular and circulatory diseases, Other non-rheumatic valve diseases, Pulmonary Arterial Hypertension, Rheumatic heart disease, Stroke, Subarachnoid hemorrhage across multiple countries. Measuring Deaths in Rate/100k

Context

The Heart Attack Risk Prediction Dataset serves as a valuable resource for delving into the intricate dynamics of heart health and its predictors. Heart attacks, or myocardial infarctions, continue to be a significant global health issue, necessitating a deeper comprehension of their precursors and potential mitigating factors. This dataset encapsulates a diverse range of attributes including age, cholesterol levels, blood pressure, smoking habits, exercise patterns, dietary preferences, and more, aiming to elucidate the complex interplay of these variables in determining the likelihood of a heart attack. By employing predictive analytics and machine learning on this dataset, researchers and healthcare professionals can work towards proactive strategies for heart disease prevention and management. The dataset stands as a testament to collective efforts to enhance our understanding of cardiovascular health and pave the way for a healthier future.

Content

This synthetic dataset provides a comprehensive array of features relevant to heart health and lifestyle choices, encompassing patient-specific details such as age, gender, cholesterol levels, blood pressure, heart rate, and indicators like diabetes, family history, smoking habits, obesity, and alcohol consumption. Additionally, lifestyle factors like exercise hours, dietary habits, stress levels, and sedentary hours are included. Medical aspects comprising previous heart problems, medication usage, and triglyceride levels are considered. Socioeconomic aspects such as income and geographical attributes like country, continent, and hemisphere are incorporated. The dataset, consisting of 8763 records from patients around the globe, culminates in a crucial binary classification feature denoting the presence or absence of a heart attack risk, providing a comprehensive resource for predictive analysis and research in cardiovascular health.

Dataset Glossary (Column-wise)

• Patient ID - Unique identifier for each patient
• Age - Age of the patient
• Sex - Gender of the patient (Male/Female)
• Cholesterol - Cholesterol levels of the patient
• Blood Pressure - Blood pressure of the patient (systolic/diastolic)
• Heart Rate - Heart rate of the patient
• Diabetes - Whether the patient has diabetes (Yes/No)
• Family History - Family history of heart-related problems (1: Yes, 0: No)
• Smoking - Smoking status of the patient (1: Smoker, 0: Non-smoker)
• Obesity - Obesity status of the patient (1: Obese, 0: Not obese)
• Alcohol Consumption - Level of alcohol consumption by the patient (None/Light/Moderate/Heavy)
• Exercise Hours Per Week - Number of exercise hours per week
• Diet - Dietary habits of the patient (Healthy/Average/Unhealthy)
• Previous Heart Problems - Previous heart problems of the patient (1: Yes, 0: No)
• Medication Use - Medication usage by the patient (1: Yes, 0: No)
• Stress Level - Stress level reported by the patient (1-10)
• Sedentary Hours Per Day - Hours of sedentary activity per day
• Income - Income level of the patient
• BMI - Body Mass Index (BMI) of the patient
• Triglycerides - Triglyceride levels of the patient
• Physical Activity Days Per Week - Days of physical activity per week
• Sleep Hours Per Day - Hours of sleep per day
• Country - Country of the patient
• Continent - Continent where the patient resides
• Hemisphere - Hemisphere where the patient resides
• Heart Attack Risk - Presence of heart attack risk (1: Yes, 0: No)

Structure of the Dataset

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Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world scenarios. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation.

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This statistic displays the mortality rate from coronary heart disease in the United Kingdom in 2023, by country. In that year, Scotland had the highest death rate from the disease, with *** deaths per 100,000 population.

Rheumatic heart disease (RHD), the principal long-term sequel of acute rheumatic fever (ARF), has been a major contributor to cardiac-related mortality in general population, especially in developing countries. With improvement in health and sanitation facilities across the globe, there has been almost a 50% reduction in mortality rate due to RHD over the last 25 years. However, recent estimates suggest that RHD still results in more than 300,000 deaths annually. In India alone, more than 100,000 deaths occur due to RHD every year (Watkins DA et al., N Engl J Med, 2017). Children and adolescents (aged below 15 years) constitute at least one-fourth of the total population in India. Besides, ARF is, for the most part, a pediatric disorder. The pediatric population, therefore, requires special consideration in developing countries to reduce the burden of RHD. In the developed world, Kawasaki disease (KD) has emerged as the most important cause of acquired heart disease in children. Mirroring global trends over the past two decades, India also has witnessed a surge in the number of cases of KD. Similarly, many regions across the globe classified as “high-risk” for ARF have witnessed an increasing trend in the incidence of KD. This translates to a double challenge faced by pediatric health care providers in improving cardiac outcomes of children affected with ARF or KD. We highlight this predicament by reviewing the incidence trends of ARF and KD over the last 50 years in ARF “high-risk” regions.

This statistic displays the mortality rate from cardiovascular disease in the United Kingdom in 2023, by country. In that year, Scotland had the highest death rate from the disease, with *** deaths per 100,000 population.

Abstract Background The COVID-19 pandemic has disrupted the delivery of care for cardiovascular diseases in Latin America. However, the effect of the pandemic on the cardiac diagnostic procedure volumes has not been quantified. Objective To assess (1) the impact of COVID-19 on cardiac diagnostic volumes in Latin America and (2) determine its relationship with COVID-19 case incidence and social distancing measures. Methods The International Atomic Energy Agency conducted a worldwide survey assessing changes in cardiac diagnostic volumes resulting from COVID-19. Cardiac diagnostic volumes were obtained from participating sites for March and April 2020 and compared to March 2019. Social distancing data were collected from Google COVID-19 community mobility reports and COVID-19 incidence per country from the Our World in Data. Results Surveys were conducted in 194 centers performing cardiac diagnostic procedures, in 19 countries in Latin America. Procedure volumes decreased 36% from March 2019 to March 2020, and 82% from March 2019 to April 2020. The greatest decreases occurred in echocardiogram stress tests (91%), exercise treadmill tests (88%), and computed tomography calcium scores (87%), with slight variations between sub-regions of Latin America. Changes in social distancing patterns (p < 0.001) were more strongly associated with volume reduction than COVID-19 incidence (p = 0.003). Conclusions COVID-19 was associated with a significant reduction in cardiac diagnostic procedures in Latin America, which was more related to social distancing than to the COVID-19 incidence. Better balance and timing of social distancing measures and planning to maintain access to medical care is warranted during a pandemic surge, especially in regions with high cardiovascular mortality.

Cardiovascular diseases (CVDs) are the leading cause of death in India, with heart attacks (myocardial infarctions) accounting for a significant portion. India has a higher heart disease burden than many other nations, with cases occurring at younger ages compared to Western countries. This dataset incorporates key medical and lifestyle risk factors such as diabetes, hypertension, obesity, smoking, air pollution exposure, and healthcare access.

With a diverse representation across India's states, the dataset reflects the urban-rural disparity in healthcare, lifestyle patterns, and emergency response times. It can be used for predictive modeling, machine learning applications, epidemiological research, and policy analysis to improve early detection and intervention strategies for heart disease.

Global Hospital Discharges for Pulmonary Heart Disease and Diseases of Pulmonary Circulation by Country, 2023 Discover more data with ReportLinker!

To assess the burden of ischemic heart disease (IHD) in Belt and Road Initiative countries from 1990 to 2021 and to predict future trends. Utilizing data from GBD 2021, we analyzed incidence, mortality, and other indicators using Joinpoint analysis, NetDrift methodology, age-period-cohort models, and Bayesian Age-Period-Cohort (BAPC) predictive modeling. In 2021, China bore the heaviest IHD burden with an incidence of 7.3 million cases and a mortality rate of 1.96 million. The United Arab Emirates experienced over a tenfold increase in incidence/prevalence rates, while Djibouti saw a fivefold rise in deaths/DALYs. Uzbekistan’s standardized incidence rate increased by 107.2%, and Lesotho’s mortality rate rose by 78.7%. A higher proportion of affected individuals were male or aged over 60 years; aging populations and demographic growth emerged as primary driving factors. Low/medium-low SDI regions exhibited significant potential for improvement; emerging risks included renal insufficiency and low grain diets. Projections indicate that by 2046, the number of IHD cases in China may exceed 14,410,378 individuals, highlighting a severe prevention and control challenge. The burden of IHD demonstrates regional disparities characterized by the interplay between traditional risk factors and emerging threats. It is recommended to establish a stratified governance framework that enhances cross-national data sharing and facilitates the transfer of digital health technologies to optimize cardiovascular disease prevention strategies.

Global Hospital Discharges for Heart Failure Cases by Country, 2023 Discover more data with ReportLinker!

Global Hospital Average Length of Stay for Heart Failure Cases by Country, 2023 Discover more data with ReportLinker!

Abstract Background: Portuguese-speaking countries (PSC) share the influence of the Portuguese culture but have socioeconomic development patterns that differ from that of Portugal. Objective: To describe trends in cardiovascular disease (CVD) morbidity and mortality in the PSC between 1990 and 2016, stratified by sex, and their association with the respective sociodemographic indexes (SDI). Methods: This study used the Global Burden of Disease (GBD) 2016 data and methodology. Data collection followed international standards for death certification, through information systems on vital statistics and mortality surveillance, surveys, and hospital registries. Techniques were used to standardize causes of death by the direct method, as were corrections for underreporting of deaths and garbage codes. To determine the number of deaths due to each cause, the CODEm (Cause of Death Ensemble Model) algorithm was applied. Disability-adjusted life years (DALYs) and SDI (income per capita, educational attainment and total fertility rate) were estimated for each country. A p-value

Background: Cardiovascular disease is the leading cause of death worldwide and a major barrier to sustainable human development. The objective of this study was to evaluate the global, sex, age, region, and country-related cardiovascular disease (CVD) burden, as well as the trends, risk factors, and implications for the prevention of CVD.Methods: Detailed information from 1990 to 2017, including global, regional, and national rates of CVD, and 11 categories of mortality and disability-adjusted life years (DALYs) were collected from the Global Burden of Disease Study 2017. The time-dependent change in the trends of CVD burdens was evaluated by annual percentage change.Results: More than 17 million people died from CVD in 2017, which was approximately two times as many as cancer, and increased nearly 50% compared with 1990. Ischemic heart disease and stroke accounted for 85% of the total age-standardized death rate (ASDR) of CVD. The ASDR and age-standardized DALYs rate (ASYR) of CVD were 1.5 times greater in men compared with women. People over the age of 50 were especially at risk for developing CVD, with the number of cases and deaths in this age group accounting for more than 90% of all age groups. CVD mortality was related to regional economic development and the social demographic index. In regions with a high economic income or socio-demographic index, there was a greater decline in the ASDR of CVD. The ASDR of CVD in high SDI regions decreased more than 50% from 1990 to 2017. Tobacco use, diets low in whole grains, diets high in sodium, and high systolic blood pressure were the important risk factors related to CVD mortality.Conclusions: CVD remains a major cause of death and chronic disability in all regions of the world. Ischemic heart disease and stroke account for the majority of deaths related to CVD. Although the mortality rate for CVD has declined in recent years from a global perspective, the results of CVD data in 2017 suggest that the mortality and DALYs of CVD varied in different ages, sexes, and countries/regions around the world. Therefore, it is necessary to elucidate the specific characteristics of global CVD burden and establish more effective and targeted prevention strategies.

****Context****

Heart disease is easier to treat when detected early, heart disease is the leading cause of death in the all over the world. The term “heart disease” refers to several types of heart conditions. In the Pakistan and some other countries, the most common type of heart disease is coronary artery disease (CAD), which can lead to heart attack. You can greatly reduce your risk for heart disease through lifestyle changes. I have uploaded this dataset together to call my fellow data scientists to run their NLP algorithms and Kernels to find and explore the heart failure predictions etc. by them selves.

****Content****

The data contains complete history of heart patients (so data scientists from different parts of the world can work with it). The dataset is collected from Pakistan, Faisalabad hospital named institute of cardiology.

****### About this dataset**** - Age : Age of the patient - Age Group: Such as 21-30 and 31-40 are grouped - Gender : Sex of the patient - Locality: Rural or Urban - Marital status: Married or unmarried
- Smoking: yes/no - Depression: yes/no - Mortality: Died= 0 and Alive= 1 - Follow.Up: Number of visiting time
- cp : Chest Pain type chest pain type - Diabetes: 0 means yes and 1 means no - chol : cholestoral in mg/dl fetched via BMI sensor - fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) - trestbps : resting blood pressure (in mm Hg) - rest_ecg : 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 - thalach : maximum heart rate achieved - PLATELET_COUNT - Hemoglobin and much more, see dataset

Inspiration

Here are some ideas to explore: 1. Create a model for predicting mortality caused by Heart Failure. 2. Can we compare the mortality rate with other famous old datasets and see the correlation 3. Can we compare the genders and ages with other datasets of multiple cities of Pakistan to see the resemblance 4. Can we compare the genders and ages with other datasets of multiple countries to see the resemblance

Any other ideas you can think of

I am looking forward to see your work and ideas and will keep adding more ideas to explore

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This statistic shows the number of deaths due to heart attacks in the European Union (EU) in 2014, by country. In Germany 121,477 people died as a result of a heart attack, followed by Italy with 69,653 people.

No. of Deaths: Caused by: All Other Forms of Heart Disease data was reported at 1,259.000 Person in Sep 2024. This records a decrease from the previous number of 1,378.000 Person for Jun 2024. No. of Deaths: Caused by: All Other Forms of Heart Disease data is updated quarterly, averaging 1,141.500 Person from Mar 2017 (Median) to Sep 2024, with 30 observations. The data reached an all-time high of 1,378.000 Person in Jun 2024 and a record low of 918.000 Person in Jun 2020. No. of Deaths: Caused by: All Other Forms of Heart Disease data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.G012: Number of Deaths: Cause of Death.

Ischemic heart disease (IEC-9: 410-414 and CIE10: I20-I25)* was the leading cause of death and responsible for 10 % of deaths. Between 1990 and 2004, mortality from this cause has decreased annually by 2.5 per cent. The territorial distribution of mortality from ischemic heart disease does not have a marked geographical pattern, although clusters of high-risk areas predominated in the north.

The North America Artificial Intelligence In Cardiology Market would witness market growth of 31.7% CAGR during the forecast period (2025-2032). The US market dominated the North America Artificial Intelligence In Cardiology Market by Country in 2024, and would continue to be a dominant market til