Heart Attack Prediction Analysis

Description

Creating machine learning model analysis using logistic regression and run the streamlit apps to predict the probability of having heart attack in future.

• Model training - Machine learning
• Method: Logistic Regression
• Deployment apps: Streamlit

In this analysis, dataset used from https://www.kaggle.com/datasets/rashikrahmanpritom/heart-attack-analysis-prediction-dataset.

About The Dataset:

age: Age of the patient

sex: Sex of the patient

cp: Chest pain type, 0 = Typical Angina, 1 = Atypical Angina, 2 = Non-anginal Pain, 3 = Asymptomatic

trtbps: Resting blood pressure (in mm Hg)

chol: Cholestoral in mg/dl fetched via BMI sensor

fbs: (fasting blood sugar > 120 mg/dl), 1 = True, 0 = False

restecg: Resting electrocardiographic results, 0 = Normal, 1 = ST-T wave normality, 2 = Left ventricular hypertrophy

thalachh: Maximum heart rate achieved

oldpeak: Previous peak

slp: Slope

caa: Number of major vessels

thall: Thalium Stress Test result ~ (0,3)

exng: Exercise induced angina ~ 1 = Yes, 0 = No

output: Target variable 0:"LOW", 1:"HIGH"

Correlation of each dataset:

Prediction accuracy:

By using logistic regression method, we get the percentace of accuracy 87%

How to run the Streamlit apps

You may clone the repository and open the Streamlit apps to test the prediction.

Steps to run the Streamlit apps:

1. Open command prompt
2. Activate environment Example: conda activate tf_env
3. Change directory to your folder path Example: cd C:\Users\snaff\OneDrive\Desktop\project 1\HeartAttack_Analysis
4. Run streamlit of your folder Example: streamlit run HeartAttack_Prediction_Deployment.py
5. Streamlit apps will appear automatically on your browser

You may insert the patient information details to check the prediction and click "submit" button for the result.

Enjoy!