Article

Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids

2020-08-04

Abstract excerpt

Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping pa...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 1fa2fbd5-e7b2-50a8-a347-b7c7dbd8284d
DOI: 10.20944/preprints202008.0074.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-MedoidsDOI 10.20944/preprints202008.0074.v1Read

K-means cluster analysis to support diabetic patient careSemantic proximity Read

Studying the cases of heart and arteries nutrition for cardiac patients using bioinformatics toolsSemantic proximity Read

Etiological and Seasonal trend in Cardiovascular Disorder displays potential risk contributing towards the development of myocardial infarctionSemantic proximity Read

A Hybrid Approach for Cardiovascular Disease Diagnosis: J48 ClassifierSemantic proximity Read

Classification of atherosclerotic and non-atherosclerotic individuals using multiclass state vector machine.Semantic proximity Read

Predicting Cardiovascular Disease with Machine Learning: A Comparative Analysis of Classification ModelsSemantic proximity Read

High-performance Cardiovascular Medicine: Artificial Intelligence for Coronary Artery DiseaseSemantic proximity Read

Enhancing Cardiovascular Disease Diagnosis through Bioinformatics and Machine LearningSemantic proximity Read

Reliable Disease Prediction System Based on Association Rule Mining and Pyramid Data StructureSemantic proximity Read

Exploring the trend of age-standardized mortality rates from cardiovascular disease in Malaysia: A joinpoint analysis (2010-2021)Semantic proximity Read

Prediction of Heart Disease Using Data Mining ClassifiersSemantic proximity Read

Prediction of Heart Disease using Data Mining ClassifiersSemantic proximity Read