/Using-Machine-Learning-Models-on-Cardiac-Arrhythmia-Data-Set

Using Machine Learning Models on Cardiac Arrhythmia Data Set

Primary LanguageJupyter Notebook

Project Name: Cardiac Arrhythmia

Problem Statement:

The Electrocardiogram (ECG) is an established technique in cardiology for the analysis of cardiac condition of the patients. In its basic definition, ECG is the electrical representation of the contractile activity of the heart, and can be recorded fairly easily by using surface electrodes on the limbs or chest of the patient. The ECG is one of the most recognized and used biomedical signal in the field of medicine. The rhythm of the heart in terms of beats per minute (bpm) can be easily calculated by counting the R peaks of the ECG wave during one minute of recording. More importantly, rhythm and the morphology of the ECG waveform is altered by cardiovascular diseases and abnormalities such as the cardiac arrhythmias, which their automatic detection and classification is the main focus of this study.

The aim of this project is to distinguish between the presence and absence of cardiac arrhythmia and to classify it in one of the 16 groups.

  Class 01 refers to NORMAL ECG
  
  Classes 02 -15 refers to different classes of arrhythmia 
  
  Class 16 refers to the rest of unclassified ones.

Class Distribution:

   Class code :   Class   :                       Number of instances:
   01             Normal                                        245
   02             Ischemic changes (Coronary Artery Disease)    44
   03             Old Anterior Myocardial Infarction            15
   04             Old Inferior Myocardial Infarction            15
   05             Sinus tachycardy                              13
   06             Sinus bradycardy                              25
   07             Ventricular Premature Contraction (PVC)       3
   08             Supraventricular Premature Contraction        2
   09             Left bundle branch block                      9	
   10             Right bundle branch block                     50
   11             1. degree AtrioVentricular block              0	
   12             2. degree AV block                            0
   13             3. degree AV block                            0
   14             Left ventricule hypertrophy                   4
   15             Atrial Fibrillation or Flutter                5
   16             Others                                        22

There are some softwares making such a classification. However there are differences between the cardiologist's and the programs classification. Taking the cardiologist's as a gold standard we aim to minimize this difference by means of machine learning tools."

Data set:

The data set of project is from UC Irvine Machine Learning Repository. https://archive.ics.uci.edu/ml/machine-learning-databases/arrhythmia/arrhythmia.data

Cardiac Arrythmia Database contains 279 attributes, 206 of which are linear valued and the rest are nominal.

Number of Instances: 452 Number of Attributes: 279

The CSV file uploaded to Github repository as well. https://github.com/MuzafferEstelik/Capstone_Project_1/blob/master/arrhythmia.csv