Authors :
Gagandeep; Dapinty Saini; Shubhpreet Kaur; Manmohan Singh
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
https://tinyurl.com/4etkjyt7
Scribd :
https://tinyurl.com/4d6hme29
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24FEB155
Abstract :
The data explosion hasushered in a new era
where insights are mined from vast data pools known as
big data. Strategies for harnessing this data have
emerged as critical decision-making tools across fields,
employing various data analysis methods. Data mining
techniques play an essential role in extracting
meaningful patterns and insights. Thispaper focuses on
the intersection of data mining and healthcare,
particularly the critical concern of heart disease
prediction.It presents a novel system that estimates heart
attack risk, combining data mining with machine
learning. Employing classification, the system stratifies
data intotwo classes: heart disease presence or absence.
Two powerful algorithms,decision tree classification and
Naïve Bayes classification, enhance accuracy in
predicting heart disease risk, achieving up to 91% and
87% accuracy, respectively. This review paper
comprehensively analyzes the system's architecture,
methodologies, and outcomes in healthcare, emphasizing
data mining and machine learning's potential in
medicine. Subsequent sections delve into methodology,
results, and implications,providing a holistic view of this
innovativeapproach.
Keywords :
Data Proliferation, Big Data, Data Mining, Machine Learning, Heart Disease Prediction, Healthcare, Classification, Decision Tree, Naïve Bayes, Predictive Modeling, Medical Science, Data Analysis, Pattern Extraction, Innovative Healthcare, Precision Medicine, Predictive Algorithms.
The data explosion hasushered in a new era
where insights are mined from vast data pools known as
big data. Strategies for harnessing this data have
emerged as critical decision-making tools across fields,
employing various data analysis methods. Data mining
techniques play an essential role in extracting
meaningful patterns and insights. Thispaper focuses on
the intersection of data mining and healthcare,
particularly the critical concern of heart disease
prediction.It presents a novel system that estimates heart
attack risk, combining data mining with machine
learning. Employing classification, the system stratifies
data intotwo classes: heart disease presence or absence.
Two powerful algorithms,decision tree classification and
Naïve Bayes classification, enhance accuracy in
predicting heart disease risk, achieving up to 91% and
87% accuracy, respectively. This review paper
comprehensively analyzes the system's architecture,
methodologies, and outcomes in healthcare, emphasizing
data mining and machine learning's potential in
medicine. Subsequent sections delve into methodology,
results, and implications,providing a holistic view of this
innovativeapproach.
Keywords :
Data Proliferation, Big Data, Data Mining, Machine Learning, Heart Disease Prediction, Healthcare, Classification, Decision Tree, Naïve Bayes, Predictive Modeling, Medical Science, Data Analysis, Pattern Extraction, Innovative Healthcare, Precision Medicine, Predictive Algorithms.
