Dataset

This dataset was created by Nagaveda Reddy

Contents

This heart disease dataset is curated by combining 3 popular heart disease datasets. The first dataset (Collected from Kaggle) contains 70000 records with 11 independent features which makes it the largest heart disease dataset available so far for research purposes. These data were collected at the moment of medical examination and information given by the patient. Second and third datasets contain 303 and 293 intstances respectively with 13 common features. The three datasets used for its curation are:Cardio Data (Kaggle Dataset)

About Dataset: This dataset is a heart disease database similar to a database already present in the repository (Heart Disease databases) but in a slightly different form.

Cite at: Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Context: The leading cause of death in the developed world is heart disease. Therefore there needs to be work done to help prevent the risks of of having a heart attack or stroke.

Content: Use this dataset to predict which patients are most likely to suffer from a heart disease in the near future using the features given.

Acknowledgement: This data comes from the University of California Irvine's Machine Learning Repository at https://archive.ics.uci.edu/ml/datasets/Heart+Disease.

Analysis of Coronary Artery Disease Risk Factors

Impact of Sex, Age, and Other Contributing Factors

By IBM Watson AI XPRIZE - Health [source]

About this dataset

Welcome to the UCI Machine Learning Repository, where you can find hundreds of datasets curated by experienced researchers for use in predictive and inferential analyses. Today, we will explore a dataset covering coronary artery disease risk factors from the Swiss Medical Center and V.A. Medical Center in Long Beach and Cleveland Clinic Foundation. This repository, collected by David W. Aha of the University of California at Irvine's School of Information and Computer Science, contains information about patients with coronary artery disease including age, sex, blood pressure, cholesterol levels, and other key variables that might be pertinent to risk assessment or prediction of outcomes for different interventions or therapies—all important factors when it comes to cardiovascular health!

Included in this dataset are attributes such as age (in years), sex (1 = male; 0 = female), chest pain type (1 = typical angina; 2 = atypical angina; 3 = non-anginal pain); 4 = asymptomatic), exercise induced angina (1 = yes; 0 = no), number of major vessels (0-3) colored by flourosopy , fasting blood sugar > 120 mg/dl (1= true; 0= false) ST depression induced by exercise relative to rest maximum heart rate achieved ,and target (0= no disease; 1-4 increasing severity). The names and social security numbers were removed from the original database but dummy values have replaced them for identification purposes. This dataset offers an opportunity for clinical professionals as well as machine learning experts alike to perform predictive analytics on large populations quickly helping medical providers make data driven decisions in order to improve patient care across diagnoses through analyzing patient characteristics - care that can mean life or death for someone suffering with cardiovascular diseases like heart failure due to clogged arteries! Are you up for the task?

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How to use the dataset

This dataset contains information on patients at risk for coronary artery disease (CAD). The data includes medical records of the patient's age, sex, blood pressure, cholesterol levels,smoking history and other risk factors. It is suitable for researchers who are interested in exploring potential risk factors that are linked with CAD.

The analysis of this dataset requires a few simple steps: - Read through the dataset to familiarize yourself with the available data points. Understanding the relevant information will help you to gain insights from your analysis. - Clean and prepare your data so it is in a usable format by removing any unnecessary columns or attributes that are not relevant to your research question(s). This step will help simplify your dataset so you can focus on specific points during your analysis. - Choose a statistical technique such as linear regression or logistic regression to analyze relationships between different attributes (e.g., age, gender and other risk factors) and determine which ones contribute most significantly to CAD development or advancement. Visualization tools such as scatter plots can also be used if desired for further exploration into relationships between variables/attributes
4 Identify patterns or correlations found in the relationships between variables/attributesin order that these can be used as independent predictors of CAD within an overall risk assessment frameworkfor example using decision trees or neural networks. You may needto use additional techniques like machine learning algorithms to support modeling predictions if required; howeverthis depends onthe research objectives beinginvestigated.]

Research Ideas

• Using this dataset, researchers could analyze the correlation between age, chest pain type, exercise induced angina and maximum heart rate achieved to identify patients at risk of developing coronary artery disease.
• The dataset could be used to explore the impact of family history of coronary artery disease on a person’s risk for developing this condition.
• The data can be used to develop and test machine learning algorithms that can accurately predict heart attack or stroke risks in individuals with certain characteristics or thresholds in various attributes such as resting blood pressure levels, cholesterol levels, etc.

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

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