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Heart Health Vital Signs and Clinical Indicators
Patient Health Records & Digital Health
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About
Unifying heart disease data from various repositories facilitates advanced analytics for the early detection and intervention of cardiovascular conditions. By merging disparate records from clinical repositories and machine learning databases, this collection offers a rich resource for uncovering patterns in cardiac health. It serves as a vital tool for medical research, allowing for the scrutiny of genetic predispositions and lifestyle factors that contribute to global morbidity. The integration process ensures that clinical indicators from diverse patient backgrounds are harmonised into a single framework for robust predictive modelling.
Columns
- ID: A unique numerical identifier for each patient record.
- Name: The full name of the individual associated with the health metrics.
- Age: The age of the participant in years, primarily ranging from 30 to 60.
- Gender: The biological sex of the individual.
- Height cm: The stature of the participant measured in centimetres.
- Weight kg: The body mass of the participant measured in kilograms.
- Blood Pressure mmHg: Clinical readings of systolic and diastolic pressure.
- Cholesterol mg/dL: Measured levels of cholesterol in the blood.
- Glucose mg/dL: Blood sugar levels recorded in milligrams per decilitre.
- Smoker: A boolean indicator signifying whether the individual is a current smoker.
Distribution
The information is delivered in a CSV file titled
Heart_health new.csv with a size of approximately 41.81 kB. It contains 724 valid records across 12 columns, demonstrating high integrity with 100% validity and no reported missing or mismatched entries. This specific file is part of a larger initiative that amalgamates thousands of records from major medical databases. The resource is designed for annual updates to ensure the data remains relevant for ongoing healthcare research.Usage
This resource is ideal for developing predictive models for early-stage cardiovascular disease detection and risk assessment. It is well-suited for training machine learning algorithms to discern subtle health patterns and for formulating public health policies based on extensive data analyses. Additionally, researchers can use the clinical indicators to explore the correlation between lifestyle factors, such as smoking, and vital signs like blood pressure and cholesterol to promote proactive medical interventions.
Coverage
The scope focuses on clinical heart health indicators for adults, primarily within the age range of 30 to 60 years. The data reflects a balanced demographic split, including a 50% representation of female participants. It integrates historical and clinical findings from multiple international sources to provide a standardised view of essential health metrics, though it maintains specific nuances from its individual source repositories.
License
CC0: Public Domain
Who Can Use It
Medical researchers can leverage this unified resource to fuel predictive models and study global cardiovascular trends. Clinicians may utilise the data-driven insights to inform preemptive interventions and personalised medicine. Furthermore, public health officials can find this a valuable primary source for designing initiatives aimed at reducing the burden of heart disease across diverse populations.
Dataset Name Suggestions
- CardioKill: Unified Cardiovascular Analytics Corpus
- Integrated Heart Disease Prediction Framework
- Heart Health Vital Signs and Clinical Indicators
- Multi-Source Cardiovascular Disease Research Set
- Global Heart Health Early Detection Registry
Attributes
Original Data Source: Heart Health Vital Signs and Clinical Indicators
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