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Is your feature request related to a problem? Please describe.
The feature request is related to the problem of predicting the likelihood of heart disease in patients using their medical data. Heart disease is a leading cause of death globally, and early detection can significantly improve treatment outcomes. Traditional methods of diagnosis can be time-consuming and require significant medical expertise. By using machine learning, specifically a neural network, we can automate and potentially improve the accuracy of heart disease predictions, making it easier for healthcare providers to identify at-risk patients quickly.
Describe the solution you'd like along with reference dataset.
The solution involves developing a neural network model to predict heart disease using the Dataset - https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data.
The dataset contains various patient features, such as age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise-induced angina, ST depression induced by exercise relative to rest, and other relevant medical data.
The proposed solution involves the following steps:
Data Collection
Data Preparation
Model Building
Model Evaluation
Describe alternatives you've considered
Traditional Machine Learning Algorithms: Using algorithms like logistic regression, decision trees, random forests, or support vector machines (SVM). These algorithms can be effective but may not capture complex patterns in the data as well as neural networks.
Other Deep Learning Architectures: Exploring different neural network architectures such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Units) for temporal data handling or using more complex architectures like ResNet or Inception models, although these might be overkill for the dataset size and complexity.
Feature Engineering: Manual creation of new features based on domain knowledge, which could improve the model's performance but requires significant expertise and time.
Please Assign me this issue under GSSOC.
Additional context
No response
Code of Conduct
I agree to follow this project's Code of Conduct
The text was updated successfully, but these errors were encountered:
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Is your feature request related to a problem? Please describe.
The feature request is related to the problem of predicting the likelihood of heart disease in patients using their medical data. Heart disease is a leading cause of death globally, and early detection can significantly improve treatment outcomes. Traditional methods of diagnosis can be time-consuming and require significant medical expertise. By using machine learning, specifically a neural network, we can automate and potentially improve the accuracy of heart disease predictions, making it easier for healthcare providers to identify at-risk patients quickly.
Describe the solution you'd like along with reference dataset.
The solution involves developing a neural network model to predict heart disease using the Dataset - https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data.
The dataset contains various patient features, such as age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise-induced angina, ST depression induced by exercise relative to rest, and other relevant medical data.
The proposed solution involves the following steps:
Data Collection
Data Preparation
Model Building
Model Evaluation
Describe alternatives you've considered
Traditional Machine Learning Algorithms: Using algorithms like logistic regression, decision trees, random forests, or support vector machines (SVM). These algorithms can be effective but may not capture complex patterns in the data as well as neural networks.
Other Deep Learning Architectures: Exploring different neural network architectures such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Units) for temporal data handling or using more complex architectures like ResNet or Inception models, although these might be overkill for the dataset size and complexity.
Feature Engineering: Manual creation of new features based on domain knowledge, which could improve the model's performance but requires significant expertise and time.
Please Assign me this issue under GSSOC.
Additional context
No response
Code of Conduct
The text was updated successfully, but these errors were encountered: