Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients
G. Parthiban,S. K. Srivatsa +1 more
Reads0
Chats0
TLDR
Using diabetics’ diagnosis, the system exhibited good accuracy and predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.Abstract:
Classifying data is a common task in Machine learning. Data mining plays an essential role for extracting knowledge from large databases from enterprises operational databases. Data mining in health care is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Most data mining methods depend on a set of features that define the behaviour of the learning algorithm and directly or indirectly influence the complexity of resulting models. Heart disease is the leading cause of death in the world over the past 10 years. Researches have been using several data mining techniques in the diagnosis of heart disease. Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body cannot effectively use the insulin it produces. Most of these systems have successfully employed Machine learning methods such as Naive Bayes and Support Vector Machines for the classification purpose. Support vector machines are a modern technique in the field of machine learning and have been successfully used in different fields of application. Using diabetics’ diagnosis, the system exhibited good accuracy and predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.read more
Citations
More filters
Journal ArticleDOI
Survey of Machine Learning Algorithms for Disease Diagnostic
Meherwar Fatima,Maruf Pasha +1 more
TL;DR: The comparative analysis of different machine learning algorithms for diagnosis of different diseases such as heart disease, diabetes disease, liver disease, dengue disease and hepatitis disease is provided.
Journal ArticleDOI
Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning.
TL;DR: In this article, different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset, which consists of 14 main attributes used for performing the analysis.
Journal ArticleDOI
Classification models for heart disease prediction using feature selection and PCA
TL;DR: The experimental results proved that the combination of chi-square with PCA obtains greater performance in most classifiers and the usage of PCA directly from the raw data computed lower results and would require greater dimensionality to improve the results.
Proceedings ArticleDOI
Machine Learning in Healthcare: A Review
TL;DR: Various machine learning algorithms used for developing efficient decision support for healthcare applications are reviewed to help in reducing the research gap for building efficient decisionSupport system for medical applications.
References
More filters
Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Journal ArticleDOI
Data mining and knowledge discovery: making sense out of data
TL;DR: Without a concerted effort to develop knowledge discovery techniques, organizations stand to forfeit much of the value from the data they currently collect and store.
Journal ArticleDOI
From Data Mining to Knowledge Discovery in Databases
TL;DR: An overview of this emerging field is provided, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases.
Book
Discovering Knowledge in Data: An Introduction to Data Mining
TL;DR: The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.