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https://github.com/jbangtson/heart_attack_classification

This analysis utilizes Logistic Regression to predict heart attacks 🩺❤ and examine significant factors related to heart health. The model is able to predict heart attacks with an overall accuracy of 85%, trained on around 240 heart attack cases.
https://github.com/jbangtson/heart_attack_classification

classification logistic-regression pandas python sklearn

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This analysis utilizes Logistic Regression to predict heart attacks 🩺❤ and examine significant factors related to heart health. The model is able to predict heart attacks with an overall accuracy of 85%, trained on around 240 heart attack cases.

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# Heart Attack Classification and Analysis    



This analysis utilizes Logistic Regression to predict heart attacks 🩺❤ and examine significant factors related to heart health. The model is able to predict heart attacks with an overall accuracy of 85%, trained on around 240 heart attack cases.



Key Metrics Summary:

- Overall Accuracy: 85%

- Precision (Heart Disease): 0.871

- Recall (Heart Disease): 0.844

- F1-Score (Heart Disease): 0.857 (1.0 is max and 100% accuracy, 0 is min)



![Confusion Matrix](assets/confusion_matrix_heatmap.png) 



## Chest pain, number of major vessels, and Sex: Highest impact on Heart Attacks.

![alt text](assets/feature_importance.png) 



## Receiver Operating Characteristic (ROC) curve



- AUC-ROC score quantifies the overall ability of the model to distinguish between classes:

  - AUC = 1.0: Perfect classifier. This model has an absolute difference of 0.07.

  - AUC = 0.5: No discrimination (random guessing).

  - AUC < 0.5: Worse than random guessing (the model is likely reversed in its predictions).



- True Positive Rate (TPR): Also known as sensitivity or recall, it measures the proportion of actual positives correctly predicted by the model.

  - TPR = True Positives (TP) / (True Positives (TP) + False Negatives (FN))



- False Positive Rate (FPR): It measures the proportion of actual negatives incorrectly predicted as positive.

  - FPR = False Positives (FP) / (False Positives (FP) + True Negatives (TN))



- The ROC curve plots TPR on the y-axis against FPR on the x-axis at various threshold values of the classification model.



![alt text](assets/ROC.png)



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This data is synthetic and should not be used for medical research; the purpose of this project is to study classification using logistic regression.



Source: https://www.kaggle.com/datasets/rashikrahmanpritom/heart-attack-analysis-prediction-dataset/data



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[More about the data](assets/heart_profile.html)



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- **Age**: Age of the patient

- **Sex**: Sex of the patient

- **exang**: Exercise induced angina (1 = yes; 0 = no)

- **ca**: Number of major vessels (0-3)

- **cp**: Chest Pain type

    - Value 1: Typical angina

    - Value 2: Atypical angina

    - Value 3: Non-anginal pain

    - Value 4: Asymptomatic

- **trtbps**: Resting blood pressure (in mm Hg)

- **chol**: Cholesterol in mg/dl fetched via BMI sensor

- **fbs**: Fasting blood sugar > 120 mg/dl (1 = true; 0 = false)

- **rest_ecg**: Resting electrocardiographic results

    - Value 0: Normal

    - Value 1: Having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)

    - Value 2: Showing probable or definite left ventricular hypertrophy by Estes' criteria

- **thalach**: Maximum heart rate achieved

- **target**: 0 = less chance of heart attack; 1 = more chance of heart attack