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Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network
Dengqing Zhang1,2, Yunyi Chen3, Yuxuan Chen3
1Jinjiang Hospital Affiliated to Fujian Medical University, Fujian, Jinjiang 362200, China.
Insights
This study introduces a new heart disease prediction model using deep neural networks and embedded feature selection. The model accurately identifies heart disease risk factors, offering a reliable tool for early diagnosis and patient care.
Area of Science:
- Cardiology
- Artificial Intelligence
- Machine Learning
Background:
- Heart disease poses a significant global health threat due to its high prevalence and mortality rates.
- Early prediction of heart disease using routine physical indicators is crucial for timely diagnosis and intervention.
- Manual analysis of large datasets for heart disease prediction is time-consuming and challenging.
Purpose of the Study:
- To develop an accurate and efficient model for predicting heart disease using readily available physical indicators.
- To leverage machine learning to overcome the limitations of manual data analysis in cardiovascular risk assessment.
- To identify key indicators significantly associated with heart disease for improved predictive accuracy.
Main Methods:
- A novel heart disease prediction algorithm combining embedded feature selection and deep neural networks was developed.
- Linear Support Vector Classification (LinearSVC) with L1 norm regularization was employed for feature selection.
- A deep neural network with He initializer weights was constructed for disease prediction.
- The model was evaluated on a heart disease dataset using metrics such as accuracy, recall, precision, F1-score, and AUC.
Main Results:
- The proposed model achieved high performance metrics: 98.56% accuracy, 99.35% recall, 97.84% precision, and a 0.983 F1-score.
- The average Area Under the Curve (AUC) score reached 0.983, indicating excellent predictive capability.
- The embedded feature selection effectively identified significant indicators for heart disease prediction.
Conclusions:
- The developed model demonstrates high efficiency and reliability in predicting heart disease.
- The integration of embedded feature selection and deep neural networks offers a promising approach for early cardiovascular risk assessment.
- This method can aid clinicians in making informed decisions and improving patient outcomes.
Abstract:
In recent decades, heart disease threatens people's health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an early stage has become a valuable subject. Clinically, it is essential to be sensitive to these indicators related to heart disease to make predictions and provide a reliable basis for further diagnosis. However, the large amount of data makes manual analysis and prediction taxing and arduous. Our research aims to predict heart disease both accurately and quickly through various indicators of the body. In this paper, a novel heart disease prediction model is given. We propose a heart disease prediction algorithm that combines the embedded feature selection method and deep neural networks. This embedded feature selection method is based on the LinearSVC algorithm, using the L1 norm as a penalty item to choose a subset of features significantly associated with heart disease. These features are fed into the deep neural network we built. The weight of the network is initialized with the He initializer to prevent gradient varnishing or explosion so that the predictor can have a better performance. Our model is tested on the heart disease dataset obtained from Kaggle. Some indicators including accuracy, recall, precision, and F1-score are calculated to evaluate the predictor, and the results show that our model achieves 98.56%, 99.35%, 97.84%, and 0.983, respectively, and the average AUC score of the model reaches 0.983, confirming that the method we proposed is efficient and reliable for predicting heart disease.
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