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Custom machine learning algorithm for large-scale disease screening - taking heart disease data as an example
Leran Chen1, Ping Ji2, Yongsheng Ma3
1Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, China; The Hong Kong Polytechnic University, Department of Industrial and Systems Engineering, Hong Kong, China.
Insights
This study introduces a patient-specific machine learning algorithm for accurate heart disease screening. The novel approach enhances detection accuracy, outperforming traditional methods for public health benefit.
Area of Science:
- Cardiology
- Artificial Intelligence
- Public Health
Background:
- Heart disease is a leading global cause of mortality, necessitating effective large-scale screening strategies.
- Current screening methods face challenges in accuracy and scalability for widespread public health initiatives.
Purpose of the Study:
- To develop and evaluate a novel patient-specific machine learning algorithm for enhanced heart disease detection.
- To customize machine learning models by focusing on data processing, neural network architecture, and loss function formulation for improved accuracy.
Main Methods:
- Developed a patient-specific machine learning algorithm integrating individual patient data.
- Customized model development across data processing, neural network architecture, and loss function.
- Validated the algorithm using the Cleveland and UC Irvine (UCI) heart disease datasets.
Main Results:
- Achieved over 95% accuracy and recall on the Cleveland dataset.
- Exceeded 97% accuracy on the UCI dataset.
- Demonstrated superior performance in medical ethics and operability compared to general-purpose machine learning algorithms.
Conclusions:
- The patient-specific machine learning algorithm offers a powerful tool for effective large-scale heart disease screening.
- This approach has the potential to significantly improve patient outcomes and reduce the economic burden of heart disease.
- The customized model customization enhances reliability and applicability in real-world clinical settings.
Abstract:
Heart disease accounts for millions of deaths worldwide annually, representing a major public health concern. Large-scale heart disease screening can yield significant benefits both in terms of lives saved and economic costs. In this study, we introduce a novel algorithm that trains a patient-specific machine learning model, aligning with the real-world demands of extensive disease screening. Customization is achieved by concentrating on three key aspects: data processing, neural network architecture, and loss function formulation. Our approach integrates individual patient data to bolster model accuracy, ensuring dependable disease detection. We assessed our models using two prominent heart disease datasets: the Cleveland dataset and the UC Irvine (UCI) combination dataset. Our models showcased notable results, achieving accuracy and recall rates beyond 95 % for the Cleveland dataset and surpassing 97 % accuracy for the UCI dataset. Moreover, in terms of medical ethics and operability, our approach outperformed traditional, general-purpose machine learning algorithms. Our algorithm provides a powerful tool for large-scale disease screening and has the potential to save lives and reduce the economic burden of heart disease.
