This project involves the classification of ECG (Electrocardiogram) readings to determine whether they are normal or abnormal. The dataset consists of rows, each representing a complete ECG of a patient with 140 data points (readings). The target variable is a categorical variable with values 0 or 1, indicating whether the ECG is normal (0) or abnormal (1).
Dataset Link: ECG Dataset
Dataset Link: ECG Dataset
Algorithm Implementation Implement machine learning algorithms for classifying ECG readings into normal or abnormal categories. Some suggested algorithms include Logistic Regression, Random Forest, Extreme Gradient Boost, K-Nearest Neighbors, Decision Tree, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory networks.
| Algorithm | Accuracy |
|---|---|
| Support Vector Machine | 0.9936 |
| Logistic Regression | 0.9872 |
| Naive Bayes | 0.964 |
| Linear Regression | 0.984 |
| XGBoost | 0.9912 |
| Random Forest | 0.9928 |
| Gradient Boosting | 0.992 |
| K-Nearest Neighbors | 0.9896 |
