Heart_Disease_Prediction

This repo is all about using Machine Learning Classifier to detect if a person has heart disease or not based on 13 given parameters

The algorithms used are as follows -

1. Random Forest Classifier (80.46 % Accuracy)
2. K-Nearest Neighbour (80.46 % Accuracy)

The input parameters are -

1. age
2. sex
3. chest pain type (4 values)
4. resting blood pressure
5. serum cholestoral in mg/dl
6. fasting blood sugar > 120 mg/dl
7. resting electrocardiographic results (values 0,1,2)
8. maximum heart rate achieved
9. exercise induced angina
10. oldpeak = ST depression induced by exercise relative to rest
11. the slope of the peak exercise ST segment
12. number of major vessels (0-3) colored by flourosopy
13. thal: 3 = normal; 6 = fixed defect; 7 = reversable defect

The dataset is present in the repository and can also be downloaded from the below Kaggle link -

https://www.kaggle.com/ronitf/heart-disease-uci

Make sure that the CSV file name on local system and inside pd.read_csv() is same. Else it will throw an error