Vaibhav Mali

Data Science Skills

Artificial Intelligence	Statistical Modeling	Text Analytics
Big Data	Digital Analytics	Visualization
Python	R	Tableau

Domain Skills

Banking and Finance	Health Care	Telecommunications
Retail	Insurance

ECG Arrhythmia Classification Using Random Forest

Category: Domain Usecases

Sub-Category: Health Care and Pharmaceuticals

Use-case Type: Structured Data Predictions

Tags: healthcare,heart_disease,irregular_heartbeat

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Note: Certain Model Inferences can take long time for the first run (Warmup) and would get faster with the subsequent inferences. We thank you for your patience.

Use Case Summary

Problem Statement :

In the medical field,Arrhythmia ofheartbeats by doctors referring to theECG Arrhythmiawill take the data directly from the ecg and predict whether patient have normal heart beats or irregular heart beats. Therefore, This model helps in understanding the creation of a system that will carry outECG data and identify thearrhythmia or normal heart beats using a machine learning approach.

Data Description :

The dataset contains features extracted two-lead ECG signal (lead II, V) from the MIT-BIH Arrhythmia dataset (Physionet). In addition, we have programmatically extracted relevant features from ECG signals to classify regular/irregular heartbeats.The dataset can be used to classify heartbeats for arrhythmia detection.

Data Source: https://www.kaggle.com/datasets/sadmansakib7/ecg-arrhythmia-classification-dataset

Model: Random Forest

Accuracy : 98%

Classification Report: