Heart Disease Dataset

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Heart Disease Dataset

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Heart Disease Dataset

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Heart Disease

Description

Explore a detailed heart disease dataset featuring key medical attributes from Cleveland, Hungary, Switzerland, and Long Beach.

Description:

Context

This heart disease dataset originates from four major databases (Cleveland, Hungary, Switzerland, and Long Beach), dating back to 1988. While the full dataset includes 76 features, most studies have focused on 14 key attributes for heart disease prediction, making it a valuable resource for machine learning models that aim to improve early diagnosis and risk assessment.

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Key Attributes

The dataset features essential medical information, including:

Age and Sex: Basic demographic data.
Chest Pain Type: Four categories, indicating different types of chest discomfort.
Resting Blood Pressure: Vital sign measurement in mmHg.
Serum Cholesterol: Total cholesterol levels in mg/dl.
Fasting Blood Sugar: Whether the fasting blood sugar exceeds 120 mg/dl.
Resting ECG: Results from the resting electrocardiogram.
Max Heart Rate: The highest heart rate achieved during exercise testing.
Exercise-Induced Angina: Presence of angina triggered by physical activity.

Additional Modern Features

To better predict heart disease in contemporary settings, this dataset could be expanded with:

Lifestyle Factors: Variables such as physical activity levels, diet, and sleep patterns.
Genetic Data: Inherited risk markers associated with cardiovascular diseases.
Smoking History and Alcohol Consumption: Habits contributing to long-term heart risk.
Mental Health and Stress Indicators: Given the known link between chronic stress and heart conditions.
Family Medical History: Family predispositions to heart disease, diabetes, and hypertension.

Applications

The enriched dataset offers comprehensive features for use in developing predictive algorithms. It is ideal for creating AI models that enhance cardiovascular disease diagnosis and offer personalized treatment plans. Researchers and healthcare providers can use this information to focus on early intervention and preventive measures, particularly for high-risk groups.

Dataset Utility

This dataset is valuable for both research and practical applications in cardiology, preventive healthcare, and personalized medicine. It can help refine the predictive accuracy of machine learning models by offering a more complete picture of a patient’s health, risk factors, and potential disease progression. By incorporating additional modern data points, the dataset can be continuously updated to remain relevant for current medical standards.