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Analysis of Blood Pressure using Data Mining Techniques
Soumyalatha Naveen
, Nayana S. Anil
, M. Prerana
, S. Shalen Janet
, V. Yashasvi
School of Computer Engineering
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INIS
accuracy
16%
blood pressure
100%
cardiovascular diseases
50%
cardiovascular system
16%
data
100%
diseases
33%
hypertension
66%
incidents
16%
interventions
16%
machine learning
16%
management
16%
mining
100%
modeling
16%
monitoring
33%
people
16%
prediction
33%
pressure measurement
16%
public health
33%
pulses
16%
risks
33%
weight
16%
Medicine and Dentistry
Blood Pressure
100%
Blood Pressure Measurement
33%
Blood Pressure Monitoring
66%
Cardiovascular Disease
100%
Cardiovascular System
33%
Chronic Disease
33%
Diseases
33%
Linear Regression Analysis
100%
Measurement Accuracy
33%
Public Health
66%
Pulse Rate
33%
Nursing and Health Professions
Blood Pressure
100%
Blood Pressure Measurement
33%
Blood Pressure Monitoring
66%
Cardiovascular Disease
100%
Cardiovascular System
33%
Chronic Disease
33%
Dependent Variable
33%
Diseases
33%
Linear Regression Analysis
100%
Measurement Accuracy
33%
Public Health
33%
Pulse Rate
33%
Engineering
Public Health
100%
Computer Science
Extensive Training
100%