Heartattack Prediction Via Machine Learning Algorithm

[MithanshuHedau]

Pull Request for PyVerse 💡

Requesting to submit a pull request to the PyVerse repository.

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issue : #727

Issue Title

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Heartattack Prediction Via Machine Learning Algorithm

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Info about the Related Issue

What's the goal of the project?
To develop a predictive model that accurately identifies individuals at risk of heart attack based on various health parameters using machine learning techniques. The model aims to assist healthcare professionals in early detection and preventive measures to reduce heart attack incidence and improve patient outcomes.

Aim :
The Heart Attack Prediction project is designed to create a machine learning-based solution capable of predicting the likelihood of an individual experiencing a heart attack based on health data and medical history. Cardiovascular diseases, particularly heart attacks, are one of the leading causes of death globally. Early prediction of heart attack risk can help in timely medical intervention and reduce mortality rates.

This project leverages a dataset containing various health metrics such as:

• Age
• Gender
• Blood pressure levels (BP)
• Cholesterol levels
• Resting heart rate
• Diabetes status
• Smoking habits
• Exercise-induced angina
• Previous heart conditions
• Electrocardiogram results (ECG)
• Body mass index (BMI)
• Physical activity levels

By analyzing these factors, the project aims to build a predictive model using machine learning algorithms like Logistic Regression, Decision Trees, Random Forest, and Artificial Neural Networks (ANN).

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Name

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Mithanshu Rajesh Hedau.

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GitHub ID

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MithanshuHedau

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Email ID

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[email protected]

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Identify Yourself

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gssoc-ext , hacktoberfest

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Closes

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Closes : #727

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Describe the Add-ons or Changes You've Made

Give a clear description of what you have added or modified.
Added the ipynb file contains the wheteher the person suffer from heart attact prediction or not . and also add Readme file for clear understanding about what my file contains about it

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Type of Change

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• [ ✅] New feature (non-breaking change which adds functionality)
• [ ✅] This change requires a documentation update

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How Has This Been Tested?

Describe how your changes have been tested.
The Heart Attack Prediction project has undergone a comprehensive testing process to ensure the accuracy and reliability of the models. The testing process includes the following steps:

Data Splitting: The dataset was divided into training and testing sets, typically using a ratio of 80:20 or 70:30. This allows for training the model on one portion of the data and evaluating it on unseen data to assess performance.

Cross-Validation: K-Fold cross-validation was employed to evaluate the models. The dataset was split into K subsets, and the model was trained K times, each time using a different subset for validation and the remaining for training. This helps to mitigate overfitting and provides a more robust estimate of model performance.

Hyperparameter Tuning: Various hyperparameters for each model were tuned using techniques like Grid Search or Random Search to identify the best combinations that yield optimal performance.

Performance Metrics: After training and validating the models, key performance metrics were calculated, including:

Accuracy: The proportion of correct predictions made by the model.
Precision: The proportion of positive identifications that were actually correct.
Recall (Sensitivity): The proportion of actual positives correctly identified.
F1-Score: The harmonic mean of precision and recall, providing a balance between the two.
ROC-AUC: The area under the Receiver Operating Characteristic curve, which indicates the model's ability to distinguish between classes.
Error Analysis: Misclassifications were analyzed to understand which types of cases the model struggled with, guiding further improvements in feature engineering or model selection.

Comparison of Models: The performance of different models was compared to identify the most effective one for predicting heart attack risk. The best-performing model was selected for deployment based on its accuracy and reliability.

Real-World Validation: If possible, the model's predictions were validated with real-world data or feedback from healthcare professionals to ensure practical applicability.

• [✅ ] I have described my testing process.

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Checklist

Please confirm the following:

• [✅ ] My code follows the guidelines of this project.
• [✅ ] I have performed a self-review of my own code.
• [ ✅] I have commented my code, particularly wherever it was hard to understand.
• [ ✅] I have made corresponding changes to the documentation.
• [ ✅] My changes generate no new warnings.
• [ ✅] I have added things that prove my fix is effective or that my feature works.
• [✅ ] Any dependent changes have been merged and published in downstream modules.

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[ruhi47]

Great work! But please provide either .csv file of the dataset in the directory or steps to read the dataset from Kaggle in the README.md file.
After that will merge the changes.