Paper Info
Title
Towards Comparing And Using Machine Learning Techniques For Detecting And Predicting Heart Attack And Diseases
Abstract
Heart diseases are one of the deadly but are silent killers for humans, which results in the increase in death rate of sufferers every year. The World Health Organization (WHO), in the year 2016, reported that 17.9 million deaths that occur worldwide per year are a result of heart disease. In the health care sector, enormous data are being generated on a daily basis, which contains different types of data, and acquiring knowledge from these data is essential. This knowledge can be acquired using various data mining techniques to mine knowledge by designing models from the medical records dataset. We implement a machine learning based system that can detect and predict heart diseases in patients using the medical records of patients. The proposed solution is based on existing techniques like Random Forest Bayesian Classification and Logistic Regression, which provides a decision support system for medical professionals to detect and predict heart diseases and heart attacks in humans or individuals using risk factors of heart disease. The dataset used in our model consists of 18 features (risk factors) and 1990 observations after performing preprocessing. It was then split into 80% train sets and 20% test sets. Using real medical records of patients, a series of experiments were conducted to examine the performance and accuracy of the proposed system. "the system was implemented in RStudio platform which predicts the risk of heart disease in patients. The compared results showed that the system performance and accuracy are acceptable with heart disease classification accuracy of 92.44% for Random Forest, 61.96%, and 59.7% for Naive Bayes Classifier and Logistic Regression, respectively.
Year
DOI
Venue
2019
10.1109/BigData47090.2019.9005488
2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
Keywords
Field
DocType
Heart Disease, Heart Attack, Classification, Random Forest, Logistic Regression, Naive Bayes Classifier, Predicted Results, Decision Support System
Health care,Data mining,Naive Bayes classifier,Computer science,Decision support system,Artificial intelligence,Medical record,Random forest,Logistic regression,Machine learning,Heart disease,Mortality rate
Conference
ISSN
Citations 
PageRank 
2639-1589
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Thankgod Obasi100.34
M. Omair Shafiq213918.59