Heartattack Prediction Via Machine Learning Algorithm #751
+2,248
−0
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Pull Request for PyVerse 💡
Requesting to submit a pull request to the PyVerse repository.
issue : #727
Issue Title
Please enter the title of the issue related to your pull request.
Heartattack Prediction Via Machine Learning Algorithm
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:
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).
Name
Please mention your name.
Mithanshu Rajesh Hedau.
GitHub ID
Please mention your GitHub ID.
MithanshuHedau
Email ID
Please mention your email ID for further communication.
[email protected]
Identify Yourself
Mention in which program you are contributing (e.g., WoB, GSSOC, SSOC, SWOC).
gssoc-ext , hacktoberfest
Closes
Enter the issue number that will be closed through this PR.
Closes : #727
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
Type of Change
Select the type of change:
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.
Checklist
Please confirm the following: